| Description |
1 online resource (206 pages) : illustrations |
| Series |
Chapman & Hall/CRC machine learning & pattern recognition series |
|
Chapman & Hall/CRC machine learning & pattern recognition series.
|
| Contents |
Cover; Series; Contents; Preface; Symbol Description; Chapter 1: Introduction; Chapter 2: Partial Least Squares; Chapter 3: Canonical Correlation Analysis; Chapter 4: Hypergraph Spectral Learning; Chapter 5: A Scalable Two-Stage Approach for Dimensionality Reduction; Chapter 6: A Shared-Subspace Learning Framework; Chapter 7: Joint Dimensionality Reduction and Classification; Chapter 8: Nonlinear Dimensionality Reduction: Algorithms and Applications; Appendix Proofs; References; Back Cover |
| Summary |
Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. 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 |
| Bibliography |
Includes bibliographical references |
| Notes |
Online resource; title from PDF title page (ebrary, viewed December 26, 2013) |
| Subject |
Computational complexity.
|
|
Machine learning.
|
|
Pattern perception.
|
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Computational complexity
|
|
Machine learning
|
|
Pattern perception
|
| Form |
Electronic book
|
| Author |
Ji, Shuiwang, 1977-
|
|
Ye, Jieping
|
| ISBN |
9781439806166 |
|
1439806160 |
|
9781439806159 |
|
1439806152 |
|
