Multi-Label Dimensionality Reduction

Name: Multi-Label Dimensionality Reduction
Brand: Taylor & Francis Inc
Price: 202.75 AUD
Availability: BackOrder

By: Jieping Ye, Shuiwang Ji, Liang Sun

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Hardcover | 4 November 2013 | Edition Number 1

At a Glance

Hardcover
208 Pages

Dimensions(cm)
24.1 x 16.3 x 1.7

Hardcover

RRP $231.00

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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^® package for implementing popular dimensionality reduction algorithms.

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