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Multilinear Subspace Learning:
Dimensionality Reduction
of Multidimensional Data

Content: 296 Pages | 56 Illustrations

Authors: Haiping Lu, Konstantinos N. Plataniotis, Anastasios Venetsanopoulos

Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data gives a comprehensive introduction to both theoretical and practical aspects of MSL for the dimensionality reduction of multidimensional data based on tensors.

It covers the fundamentals, algorithms, and applications of MSL.

Bayesian Programming

Content: 380 Pages | 61 Illustrations

Authors: Pierre Bessiere, Emmanuel Mazer, Juan Manuel Ahuactzin, Kamel Mekhnacha

Emphasizing probability as an alternative to Boolean logic, Bayesian Programming covers new methods to build probabilistic programs for real-world applications.

Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many Python examples that are also available on a supplementary website together with an interpreter that allows readers to experiment with this new approach to programming.

Computational Business Analytics

Content: 516 Pages | 290 Illustrations

Author: Subrata Das

Computational Business Analytics presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains.

Through many examples and challenging case studies from a variety of fields, practitioners easily see the connections to their own problems and can then formulate their own solution strategies.

Multi-Label Dimensionality Reduction

Content: 208 Pages | 23 Illustrations

Authors: Liang Sun, Shuiwang Ji, Jieping Ye

The data mining and machine learning literature currently lacks a unified treatment of multi-label dimensionality reduction that incorporates both algorithmic developments and applications.

Addressing this shortfall, Multi-Label Dimensionality Reduction covers the methodological developments, theoretical properties, computational aspects, and applications of many multi-label dimensionality reduction algorithms.

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