Multi-Label Dimensionality Reduction

1st Edition

Liang Sun, Shuiwang Ji, Jieping Ye

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Chapman and Hall/CRC
Published April 19, 2016
Reference - 208 Pages
ISBN 9780429148200 - CAT# KE83962

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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

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