Multi-Label Dimensionality Reduction Contributor(s): Sun, Liang (Author), Ji, Shuiwang (Author), Ye, Jieping (Author) |
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ISBN: 1439806152 ISBN-13: 9781439806159 Publisher: CRC Press OUR PRICE: $133.00 Product Type: Hardcover - Other Formats Published: November 2013 |
Additional Information |
BISAC Categories: - Business & Economics | Statistics - Computers | Programming - Games - Computers | Databases - General |
Dewey: 006.31 |
LCCN: 2012276295 |
Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition |
Physical Information: 0.6" H x 6" W x 9.3" (1.40 lbs) 208 pages |
Descriptions, Reviews, Etc. |
Publisher Description: 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:
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. |