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Selecting label-dependent features for multi-label classification
- Source :
- Neurocomputing. 259:112-118
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
Abstract
- An instance is often represented from different aspects (views or modalities), which leads to high-dimensional features and even multiple labels. In this paper, we focus on the feature selection problem in multi-label classification, for which a trivial solution is handling the labels dividedly. Obviously, such a scheme may not work well by leaving the label relationship out of consideration. Recently, several research works conduct feature selection directly under a multi-label framework by implicitly or explicitly modeling label relationship. However, these works assume that all labels share the same feature subset or subspace, which is not reasonable enough for some scenarios since different labels tend to convey different semantics. To address this problem, we develop a novel approach in this paper to select label-dependent features for multi-label classification. Specifically, we (1) formulate a convex model based on a more general and practical assumption that different labels convey different semantics with specific features; (2) design an alternating optimization algorithm based on Nesterov's method and L1-ball projection for efficiently finding the optimal solution, which can realize multi-label classification, feature selection, and label relationship estimation simultaneously. Finally, experiments on publicly available datasets show that the proposed algorithm achieves better performance than several related methods.
- Subjects :
- Scheme (programming language)
0209 industrial biotechnology
Computer science
Semantics (computer science)
Cognitive Neuroscience
Feature selection
02 engineering and technology
Machine learning
computer.software_genre
020901 industrial engineering & automation
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Projection (set theory)
computer.programming_language
Multi-label classification
business.industry
Pattern recognition
Computer Science Applications
ComputingMethodologies_PATTERNRECOGNITION
Feature (computer vision)
020201 artificial intelligence & image processing
Artificial intelligence
Focus (optics)
business
computer
Subspace topology
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 259
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........67c769201d811bc0810ab0e6b6fe679a