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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
Multilinear Subspace Learning
Dimensionality Reduction in Machine Learning
Elements of Dimensionality Reduction and Manifold Learning
Dimensionality Reduction in Data Science
Linear Dimensionality Reduction
Multi-aspect Learning
Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
Regularization, Optimization, Kernels, and Support Vector Machines
Computational Methods of Feature Selection
Sufficient Dimension Reduction
Pattern Recognition Algorithms for Data Mining
Multiview Machine Learning
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