Chapman and Hall/CRC
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
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.