Search results for curse of dimensionality

Data Compression via Dimensionality Reduction: 3 Main Methods
...a? If you are in data science for quite some time, you must have heard this phrase: Dimensionality is a curse. This is also referred to as the curse of dimensionality. You can learn more about this term here. Overall, some of the common disadvantages of high dimensional data are: Overfitting...https://www.kdnuggets.com/2020/12/datacompressiondimensionalityreduction.html

Dimensionality Reduction : Does PCA really improve classification outcome?">Dimensionality Reduction : Does PCA really improve classification outcome?
...ggest fan of Machine Learning algorithms and Data Science jobs. Original. Reposted with permission. Related: Nutrition & Principal Component Analysis: A Tutorial A comparison between PCA and hierarchical clustering MustKnow: What is the curse of dimensionality?...https://www.kdnuggets.com/2018/07/dimensionalityreductionpcaimproveclassificationresults.html

MustKnow: What is the curse of dimensionality?
...thm. Think of image recognition problem of high resolution images 1280 × 720 = 921,600 pixels i.e. 921600 dimensions. OMG. And that’s why it’s called Curse of Dimensionality. Value added by additional dimension is much smaller compared to overhead it adds to the algorithm. Bottom line is, the data...https://www.kdnuggets.com/2017/04/mustknowcursedimensionality.html

Deep Learning, The Curse of Dimensionality, and Autoencoders
...ctability concerns. Dimensionality is Exponentially Worse Let's get a rough idea of how the difficulty of a machine learning problem increases as the dimensionality increases. According to a study by C.J. Stone in 1982, the time it takes to fit a model (specifically a nonparametric regression) to...https://www.kdnuggets.com/2015/03/deeplearningcursedimensionalityautoencoders.html

Understanding NLP and Topic Modeling Part 1
...) of our text data. And with this distilled signal, we can start the real work of generating insights. Let’s go through this step by step. The Curse of Dimensionality High dimensional data is regarded as a curse in many data science applications. If you want to understand why in more...https://www.kdnuggets.com/2019/11/understandingnlptopicmodelingpart1.html

Classifying Heart Disease Using KNearest Neighbors
...r is chosen if the number of classes is even. You can also check by generating the model on different values of K and check their performance. Curse of Dimensionality KNN performs better with a lower number of features than a large number of features. You can say that when the number...https://www.kdnuggets.com/2019/07/classifyingheartdiseaseusingknearestneighbors.html

What Is Dimension Reduction In Data Science?
...ytics, Data Science, Data Mining, and Machine Learning Related: The Data Science Gold Rush: Top Jobs in Data Science and How to Secure Them Dimensionality Reduction : Does PCA really improve classification outcome? MustKnow: What is the curse of dimensionality?...https://www.kdnuggets.com/2019/01/dimensionreductiondatascience.html

Tree Kernels: Quantifying Similarity Among TreeStructured Data
...of the input. In other words, their classification power decreases with an increase in the dimensionality of the input. This problem is known as the curse of dimensionality. To get an idea of the reason for this degradation of performance, consider a space X of dimension d. Suppose that X contains...https://www.kdnuggets.com/2016/02/treekernelsquantifyingsimilaritytreestructureddata.html

Beyond OneHot: an exploration of categorical variables
…is just the number of columns in the dataset, but it has significant downstream effects on the eventual models. At the extremes, the concept of the “curse of dimensionality” discusses that in highdimensional spaces some things just stop working properly. Even in relatively low dimensional…https://www.kdnuggets.com/2015/12/beyondonehotexplorationcategoricalvariables.html

Diffusion Map for Manifold Learning, Theory and Implementation
...among the variables (or features or dimensions), which we need to learn to compute better similarity. Manifold learning is an approach to nonlinear dimensionality reduction. The basis for algorithms in manifold learning is that the dimensionality of many data sets is only artificially high 1. In...https://www.kdnuggets.com/2020/03/diffusionmapmanifoldlearningtheoryimplementation.html

12 Useful Things to Know About Machine Learning">12 Useful Things to Know About Machine Learning
...ering more features never hurts, since at worst they provide no new information about the class. But in fact, their benefits may be outweighed by the curse of dimensionality. 6 — Theoretical Guarantees are Not What They Seem Machine learning papers are full of theoretical guarantees....https://www.kdnuggets.com/2018/04/12usefulthingsknowaboutmachinelearning.html

Adversarial Examples, Explained
...ensionality is? A number of phenomena that are intuitive in two or three dimensions behave unexpectedly in very high dimension—a problem known as the curse of dimensionality. In this context, the existence of adversarial examples has been argued to be a property of the highdimensional dot product:...https://www.kdnuggets.com/2018/10/adversarialexamplesexplained.html

Top stories for Mar 29 – Apr 4: Deep Learning, Dimensionality, and Autoencoders; The Grammar of Data Science: Python vs R
Most viewed news items Deep Learning, The Curse of Dimensionality, and Autoencoders  Mar 12, 2015. The Grammar of Data Science: Python vs R  Mar 28, 2015. More Free Data Mining, Data Science Books and Resources  Mar 25, 2015. Deep Learning for Text Understanding from Scratch  Mar 13, 2015....https://www.kdnuggets.com/2015/04/topnewsweekmar29.html

17 More MustKnow Data Science Interview Questions and Answers, Part 2
...tting with the reusable holdout: Preserving validity in adaptive data analysis 11 Clever Methods of Overfitting and how to avoid them Q8. What is the curse of dimensionality? Prasad Pore answers: "As the number of features or dimensions grows, the amount of data we need to generalize...https://www.kdnuggets.com/2017/02/17datascienceinterviewquestionsanswerspart2.html

fast.ai Machine Learning Course Notes
...ortunity to learn. Machine Learning 1: Lesson 1 Question: What about a curse of dimensionality? There are two concepts you often hear — curse of dimensionality and no free lunch theorem. They are both largely meaningless and basically stupid and yet many people in the field not only...https://www.kdnuggets.com/2018/07/suenagafastaimachinelearningnotes.html

Why Deep Learning Works – Key Insights and Saddle Points
...he curve." "A line is very smooth. A curve with some ups and downs is less smooth but still smooth." So, it's clear that smoothness will not beat the curse of dimensionality alone. In fact, smoothness doesn't even apply to modern, complex problems like computer vision or natural language...https://www.kdnuggets.com/2015/11/theoreticaldeeplearning.html

Deep Learning for NLP: An Overview of Recent Trends">Deep Learning for NLP: An Overview of Recent Trends
...to model natural language tasks until neural methods came around and solved some of the problems faced by traditional machine learning models such as curse of dimensionality. Word Embeddings: Distributional vectors, also called word embeddings, are based on the socalled distributional hypothesis —...https://www.kdnuggets.com/2018/09/deeplearningnlpoverviewrecenttrends.html

Explainable Artificial Intelligence (Part 2) – Model Interpretation Strategies
...ecisions in a humaninterpretable form. Dimensionality reduction techniques are very useful here since often we deal with a very large feature space (curse of dimensionality) and reducing the feature space helps us visualize and see what might be influencing a model to take specific decisions. Some...https://www.kdnuggets.com/2018/12/explainableaimodelinterpretationstrategies.html

Top stories for Mar 814: 7 common Machine Learning mistakes; Deep Learning for Text Understanding from Scratch
...chine Learning Table of Elements Decoded  Mar 11, 2015. Cartoon: US Chief Data Scientist Most Difficult Challenge  Mar 13, 2015. Deep Learning, The Curse of Dimensionality, and Autoencoders  Mar 12, 2015. KDD2017, top conference on Data Mining, Data Science Research coming to Halifax, Canada ...https://www.kdnuggets.com/2015/03/topnewsweekmar8.html

Inside Deep Learning: Computer Vision With Convolutional Neural Networks
...achine learning and the biomedical sciences. He is a two time gold medalist at the International Biology Olympiad, a student researcher, and a “hacker.” Related: Talking Machine – 3 Deep Learning Gurus Talk about History and Future of Machine Learning, part 1 Deep Learning, The Curse of...https://www.kdnuggets.com/2015/04/insidedeeplearningcomputervisionconvolutionalneuralnetworks.html

Top arXiv Papers, January: ConvNets Advances, Wide Instead of Deep, Adversarial Networks Win, Learning to Reinforcement Learn
...sh Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao This paper is an overview of neural networks, deep versus shallow architectures, and how the curse of dimensionality fits. Not exactly groundbreaking, but good review and tutorial material nonetheless. The paper characterizes classes of...https://www.kdnuggets.com/2017/02/toparxivpapersjanuaryconvnetswideadversarial.html

Feature Selection – All You Ever Wanted To Know
...ture selection techniques include: simplification of models to make them easier to interpret by researchers/users shorter training times avoiding the curse of dimensionality enhanced generalization by reducing overfitting (formally, reduction of variance) Dataset size reduction is more important...https://www.kdnuggets.com/2021/06/featureselectionoverview.html

Interview: Xinghua Lou (Microsoft) on Mining Clinical Notes and Big Data in Healthcare
...u think are the most effective ways to learn Big Data skills? XL: Just read and practice, especially practice, because you will not truly understand "curse of dimensionality" until your data makes you complain about it! AR: Q8. What book did you recently read and liked? XL: Nate Silver's The Signal...https://www.kdnuggets.com/2014/05/interviewxinghualoumachinelearningmicrosoft.html

How GOAT Taught a Machine to Love Sneakers
...alizing Data using tSNE Sampling Generative Networks Generative Adversarial Networks Original. Reposted with permission. Related: Deep Learning, The Curse of Dimensionality, and Autoencoders Efficient Graphbased Word Sense Induction Best (and Free!!) Resources to Understand Nuts and Bolts of Deep...https://www.kdnuggets.com/2018/08/goattaughtmachinelovesneakers.html

Popular Machine Learning Interview Questions">Popular Machine Learning Interview Questions
...n such a case, your bank should develop a fraud detection algorithm that decreases the FN, thus increases the recall. Q11. What’s the Curse of Dimensionality, and how to solve it? This is when your dataset has too many features, so it’s hard for your model to learn and extract those...https://www.kdnuggets.com/2021/01/popularmachinelearninginterviewquestions.html

Must Know for Data Scientists and Data Analysts: Causal Design Patterns">Must Know for Data Scientists and Data Analysts: Causal Design Patterns
...number of variables require adjustment (because they impact both the treatment likelihood and the outcome) Otherwise, we are plagued by the curse of dimensionality. Example Application: Although we could run another A/B test, we only had one shot at Black Friday and need to decide whether or not...https://www.kdnuggets.com/2021/03/causaldesignpatterns.html

Explore the world of Bioinformatics with Machine Learning">Explore the world of Bioinformatics with Machine Learning
...e have 72 rows and 7129 columns. Basically we need to decrease the number of features(Dimentioanlity Reduction) to remove the possibility of Curse of Dimensionality. For reducing the number of dimensions/features we will use the most popular dimensionality reduction algorithm i.e. PCA(Principal...https://www.kdnuggets.com/2019/09/exploreworldbioinformaticsmachinelearning.html

Exoplanet Hunting Using Machine Learning
...columns in our training dataset. Basically we need to decrease the number of features(Dimentioanlity Reduction) to remove the possibility of Curse of Dimensionality. For reducing the number of dimensions/features we will use the most popular dimensionality reduction algorithm i.e. PCA(Principal...https://www.kdnuggets.com/2020/01/exoplanethuntingmachinelearning.html

What You Are Too Afraid to Ask About Artificial Intelligence (Part I): Machine Learning
...ith two major problems, namely the credit assignment problem and the exploreexploit dilemma — plus a series of technical issues such as the curse of dimensionality, nonstationary environments, or partial observability of the problem. The former one concerns the fact that rewards are, by...https://www.kdnuggets.com/2016/12/tooafraidaskaboutartificialintelligencemachinelearning.html

Deep Learning for Text Understanding from Scratch
...gen Schmidhuber AMA: The Principles of Intelligence and Machine Learning  Facebook Open Sources deeplearning modules for Torch  Deep Learning, The Curse of Dimensionality, and Autoencoders I do however hope to see the team compare their method to stronger competitors in followup work, and keep...https://www.kdnuggets.com/2015/03/deeplearningtextunderstandingfromscratch.html

KDnuggets™ News 15:n11, Apr 15: Big Data Predictive Analytics Gainers & Losers; Awesome Public Datasets
...ories for Mar 29  Apr 4: Deep Learning, Dimensionality, and Autoencoders; The Grammar of Data Science: Python vs R  Apr 5, 2015. Deep Learning, The Curse of Dimensionality, and Autoencoders; The Grammar of Data Science: Python vs R; Data Science as a profession  time is now; Forrester Wave Big...https://www.kdnuggets.com/2015/n11.html

Watch: Basics of Machine Learning
...EM Algorithm Gaussian Example Multivariate Gaussians Multivariate Gaussians How Many Gaussians? Lectures 1819: Principal Component Analysis Curse of Dimensionality Dimensionality Reduction Direction of Greatest Variance Principal Components = Eigenvectors Finding Eigenvalues and Eigenvectors...https://www.kdnuggets.com/2014/05/watchbasicsmachinelearning.html

20 Lessons From Building Machine Learning Systems
…e can’t simply choose supervised or unsupervised learning. One has to use them simultaneously and iteratively. Unsupervised learning is good tool for dimensionality reduction and feature engineering. You need to learn the “magic” behind combining unsupervised/supervised learning. E.g.1 clustering +…https://www.kdnuggets.com/2015/12/xamat20lessonsbuildingmachinelearningsystems.html

Top /r/MachineLearning Posts, 2016: Google Brain AMA; Google Machine Learning Recipes; StarCraft II AI Research Environment
...against itself. 8. Great summary of deep learning So... what does Yann LeCun think HE does? ELI5: why doesn't deep learning suffer from the curse of dimensionality? Because deep learning was unpopular at the time, so none of the other machine learning algorithms wanted it to come along on the...https://www.kdnuggets.com/2017/01/topredditmachinelearning2016.html

A Look Back on the 1st Three Months of Becoming a Data Scientist
...s concerning memory management and big data? Conversely, a bunch of software engineers likely don’t know squat about statistical significance and the curse of dimensionality. So develop your niche such that it appeals to your interests and utilizes your past experiences. Then it’s a matter of luck...https://www.kdnuggets.com/2016/01/lookback1stthreemonthsdatascientist.html

Should Data Science Really Do That?
...se of people’s lives. This may not be a desirable outcome, as all models are likely to have some systematic biases due to data incompleteness and the curse of dimensionality. Discovering systematic biases is very tricky when thousands to millions of features are used to train nonlinear models. It...https://www.kdnuggets.com/2015/05/shoulddatasciencedothat.html

Data Science 101: Preventing Overfitting in Neural Networks
...with deep interests in machine learning and the biomedical sciences. He is a two time gold medalist at the International Biology Olympiad, a student researcher, and a “hacker.” Related: Inside Deep Learning: Computer Vision With Convolutional Neural Networks Deep Learning, The Curse of...https://www.kdnuggets.com/2015/04/preventingoverfittingneuralnetworks.html

Nando de Freitas AMA: Bayesian Deep Learning, Justice, and the Future of AI
...ble to afford rent  they work so bloody hard. Question from up7up: Is strong AI possible? What prevents its implementation? Combinatorial explosion? Curse of dimensionality? P versus NP problem? Something else? (Clarifying link added afterward by the author:...https://www.kdnuggets.com/2016/01/nandodefreitasama.html

Top KDnuggets tweets, Oct 2426: Why Deep Learning is likely to make other Machine Learning algorithms obsolete
...d t.co/aNMGrw4YF8 Free! 3 Great Data Science Books You Can Read Now t.co/AzxWnEyEEF #DataViz #BigData An interactive visualization to teach about the curse of dimensionality #rstats #DataViz t.co/LEBizaK2Jg MIT researchers use Bayesian regression to predict the price of #Bitcoin, claim it can...https://www.kdnuggets.com/2014/10/toptweetsoct2426.html

Data Science 102: Kmeans clustering is not a free lunch
...at happens to the kmeans algorithm when those assumptions are broken. We’ll stick to 2dimensional data since it’s easy to visualize. (Thanks to the curse of dimensionality, adding additional dimensions is likely to make these problems more severe, not less). We’ll work with the statistical...https://www.kdnuggets.com/2015/01/datascience102kmeansclusteringnotfreelunch.html

KDnuggets™ News 15:n09, Mar 25: Deep Learning from Scratch; 10 steps to Kaggle Success; US CDS DJ Patil Cartoon
...ng Analytics to be expressive, short, fast, define core operations that cover 90% of problems, and to be easy to follow and learn. Deep Learning, The Curse of Dimensionality, and Autoencoders  Mar 12, 2015. Autoencoders are an extremely exciting new approach to unsupervised learning and for many...https://www.kdnuggets.com/2015/n09.html

Top stories in March: 7 common Machine Learning mistakes; Deep Learning for Text Understanding from Scratch
...arning for Text Understanding from Scratch  Mar 13, 2015. More Free Data Mining, Data Science Books and Resources  Mar 25, 2015. Deep Learning, The Curse of Dimensionality, and Autoencoders  Mar 12, 2015. The Grammar of Data Science: Python vs R  Mar 28, 2015. 10 Steps to Success in Kaggle Data...https://www.kdnuggets.com/2015/04/topnews2015mar.html

Localytics: Data Scientist
...when processing data for a training set? What are the costs and benefits of different techniques? When should we expect to suffer from overfitting / curse of dimensionality? How can we preempt and compensate for it? Areas of Expertise: Proven experience evangelizing ideas to nontechnical...https://www.kdnuggets.com/jobs/15/0212localyticsdatascientist.html

Highlights of IEEE ICDM 2013 International Conference on Data Mining, Dallas
...on Clustering is hard when the dataset has many attributes/variables, mainly because distances lose their discriminative power in highdimension (see curse of dimensionality). S. Gunnemann and C. Faloutsos address this issue with "Mixed Membership Subspace Clustering", a method that makes the most...https://www.kdnuggets.com/2013/12/reportieeeicdm2013internationalconferencedataminingdallas.html

Beyond Siri, Google Assistant, and Alexa – what you need to know about AI Conversational Applications
...ps that understand broadvocabulary natural language are extremely complicated due to combinatorial complexity of language. See the phenomenon called curse of dimensionality. In my experience, AI developers have to use a combination of narrowdomain and generic training data to achieve the...https://www.kdnuggets.com/2019/04/aiconversationalapplications.html

Avoiding Obvious Insights Using Analyze With Insight Miner
...t. There are a few reasons to use feature selection techniques prior to model construction. Among them are the simplification of models, avoiding the curse of dimensionality, and reducing overfitting. While there are many feature selection techniques, we’ll focus on mutual information based...https://www.kdnuggets.com/2019/04/sisenseinsightminer.html

Neural Networks 201: All About Autoencoders
...wing a previous career as a Material Scientist in the semiconductor industry building thinfilm nanomaterials. Related: Autoencoders: Deep Learning with TensorFlow’s Eager Execution Variational Autoencoders Explained in Detail Deep Learning, The Curse of Dimensionality, and Autoencoders...https://www.kdnuggets.com/2019/11/allaboutautoencoders.html

NewAge Machine Learning Algorithms in Retail Lending">NewAge Machine Learning Algorithms in Retail Lending
…ing is commonly used to segment and profile Customer Base to better understand and develop strategy for each segment. It’s training suffers from the “Curse of Dimensionality”. While methods like PCA are applied to solve for this, Deep Learning can be an alternative to create more advanced lower…https://www.kdnuggets.com/2017/09/machinelearningalgorithmslending.html

Understanding Feature Engineering: Deep Learning Methods for Text Data
...parse word vectors for textual data and thus if we do not have enough data, we may end up getting poor models or even overfitting the data due to the curse of dimensionality. Comparing feature representations for audio, image and text To overcome the shortcomings of losing out semantics and feature...https://www.kdnuggets.com/2018/03/understandingfeatureengineeringdeeplearningmethodstextdata.html

Ten Machine Learning Algorithms You Should Know to Become a Data Scientist">Ten Machine Learning Algorithms You Should Know to Become a Data Scientist
...tially a way to calculate ordered components too, but you don’t need to get the covariance matrix of points to get it. This Algorithm helps one fight curse of dimensionality by getting datapoints with reduced dimensions. Libraries:...https://www.kdnuggets.com/2018/04/10machinelearningalgorithmsdatascientist.html

9 Reasons why your machine learning project will fail
...don’t wish ill on anyone. I am rooting for you and hope your project succeeds beyond expectations. The purpose of this article is not to put a voodoo curse on you and assure your project’s failure. Rather, it a visit to the most common reasons why data science projects do fail and hopefully help...https://www.kdnuggets.com/2018/07/whymachinelearningprojectfail.html

3 practical thoughts on why deep learning performs so well
…it in the deep learning literature, where the “Hamiltonian of the spin glass model [2]”, the exploitation of compositional functions to cope with the curse of dimensionality [3], their capability to best represent the simplicity of physicsbased functions [4], and the flattening of the data…https://www.kdnuggets.com/2017/02/whydeeplearningperformssowell.html

KDnuggets™ News 17:n15, Apr 19: Forrester vs Gartner on Data Science/Analytics Platforms; 5 Machine Learning Projects You Can No Longer Overlook
...cience Trends WordStat for Stata Now on Macs Tutorials, Overviews Time Series Analysis with Generalized Additive Models MustKnow: What is the curse of dimensionality? Predictive Maintenance: A Primer Is Blockchain the Ultimate Enabler of Data Monetization? Medical Image Analysis with Deep...https://www.kdnuggets.com/2017/n15.html

The Book of Why
...estimation is not trivial when the number of variables is large, and only bigdata and modern machinelearning techniques can help us to overcome the curse of dimensionality.” Pearl also has some harsh words for data science: “We live in an era that presumes Big Data to be the solution to...https://www.kdnuggets.com/2018/06/graypearlbookofwhy.html

The Truth About Bayesian Priors and Overfitting
...the data was allowed to drive the value to a too high value meaning that we are overfitting. This is exactly why maximum likelihood suffers from the curse of dimensionality. We shouldn’t be surprised by this since we literally told the model that a value up to 10 is quite probable. We can...https://www.kdnuggets.com/2017/07/truthaboutbayesianpriorsoverfitting.html

Author of “Everybody Lies” to Speak at Predictive Analytics World NYC
...of deployed machine learning (calling it "doppelgänger discovery"), as well as the everimportant pitfall of phacking (referring to it as "the curse of dimensionality"). The book wraps up with a hilarious and poignant conclusion at the very end, whereby the author himself practices what...https://www.kdnuggets.com/2017/07/pawnycauthoreverybodylies.html

The Practical Importance of Feature Selection">The Practical Importance of Feature Selection
..., or attributes, to be used in the predictive modeling process. Feature selection is useful on a variety of fronts: it is the best weapon against the Curse of Dimensionality; it can reduce overall training times; and it is a powerful defense against overfitting, increasing model generalizability....https://www.kdnuggets.com/2017/06/practicalimportancefeatureselection.html

Monte Carlo integration in Python">Monte Carlo integration in Python
...e of the method is independent of the number of dimensions. In machine learning speak, the Monte Carlo method is the best friend you have to beat the curse of dimensionality when it comes to complex integral calculations. Read this article for a great introduction, Monte Carlo Methods in Practice...https://www.kdnuggets.com/2020/12/montecarlointegrationpython.html

How to Deal with Missing Values in Your Dataset
...es are not similar in type (such as age, gender, height, etc.). The advantage of using KNN is that it is simple to implement. But it suffers from the curse of dimensionality. It works well for a small number of variables but becomes computationally inefficient when the number of variables is large....https://www.kdnuggets.com/2020/06/missingvaluesdataset.html