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A weighted linear discriminant analysis framework for multi-label feature extraction
•A weighted multi-label linear discriminant analysis framework (wMLDA) is bulit.•Four existing weight forms (binary, correlation, entropy and fuzzy-based) are collected.•A dependence-based weight form is proposed using Hilbert–Schmidt independence criterion.•Our dependence-based wMLDA performs the b...
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Published in: | Neurocomputing (Amsterdam) 2018-01, Vol.275, p.107-120 |
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Main Author: | |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | •A weighted multi-label linear discriminant analysis framework (wMLDA) is bulit.•Four existing weight forms (binary, correlation, entropy and fuzzy-based) are collected.•A dependence-based weight form is proposed using Hilbert–Schmidt independence criterion.•Our dependence-based wMLDA performs the best statistically on ten data sets.
Linear discriminant analysis (LDA) is one of the most popular single-label (multi-class) feature extraction techniques. For multi-label case, two slightly different generalized versions have been introduced independently. We argue whether there exists a framework to unify such two multi-label LDA methods and to derive more well-performed multi-label LDA techniques further. In this paper, we build a weighted multi-label LDA framework (wMLDA) to consolidate two existing multi-label LDA-type methods with binary and correlation-based weight forms, and further collect two additional weight forms with entropy and fuzzy principles. To exploit both label and feature information more sufficiently, via maximizing dependence based on Hilbert–Schmidt independence criterion, a novel dependence-based weight form is proposed, which is formulated as a non-convex quadratic programing problem with ℓ1-norm and non-negative constraints and then is solved by random block coordinate descent method with a linear convergence rate. Experiments on ten data sets illustrate that our dependence-based wMLDA works the best, and five wMLDA-type algorithms are superior to canonical correlation analysis and multi-label dimensionality reduction via dependency maximization, according to five multi-label classification performance measures and Wilcoxon statistical test. |
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ISSN: | 0925-2312 1872-8286 |
DOI: | 10.1016/j.neucom.2017.05.008 |