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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 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.
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