Multi-Label Dimensionality Reduction

Multi-Label Dimensionality Reduction

Author
Liang Sun, Shuiwang Ji, Jieping Ye
Publisher
Chapman and Hall/CRC
Language
English
Edition
0
Year
2013
Page
208
ISBN
978-1-4398-0616-6,978-1-4398-0615-9
File Type
pdf
File Size
3.2 MiB

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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