Multi-label dimensionality reduction
著者
書誌事項
Multi-label dimensionality reduction
（Chapman & Hall/CRC machine learning & pattern recognition series）（A Chapman & Hall book）
CRC Press, c2014
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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 developments, theoretical properties, computational aspects, and applications of many multi-label dimensionality reduction algorithms. It explores numerous research questions, including:
How to fully exploit label correlations for effective dimensionality reduction
How to scale dimensionality reduction algorithms to large-scale problems
How to effectively combine dimensionality reduction with classification
How to derive sparse dimensionality reduction algorithms to enhance model interpretability
How to perform multi-label dimensionality reduction effectively in practical applications
The authors emphasize their extensive work on dimensionality reduction for multi-label learning. Using a case study of Drosophila gene expression pattern image annotation, they demonstrate how to apply multi-label dimensionality reduction algorithms to solve real-world problems. A supplementary website provides a MATLAB (R) package for implementing popular dimensionality reduction algorithms.
目次
Introduction. Partial Least Squares. Canonical Correlation Analysis. Hypergraph Spectral Learning. A Scalable Two-Stage Approach for Dimensionality Reduction. A Shared-Subspace Learning Framework. Joint Dimensionality Reduction and Classification. Nonlinear Dimensionality Reduction: Algorithms and Applications. Appendix. References. Index.
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