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Communication Dans Un Congrès Année : 2011

Constrained Laplacian Score for semi-supervised feature selection

Khalid Benabdeslem
Mohammed Hindawi

Résumé

In this paper, we address the problem of semi-supervised feature selection from high-dimensional data. It aims to select the most discriminative and informative features for data analysis. This is a recent addressed challenge in feature selection research when dealing with small labeled data sampled with large unlabeled data in the same set. We present a filter based approach by constraining the known Laplacian score. We evaluate the relevance of a feature according to its locality preserving and constraints preserving ability. The problem is then presented in the spectral graph theory framework with a study of the complexity of the proposed algorithm. Finally, experimental results will be provided for validating our proposal in comparison with other known feature selection methods.

Dates et versions

hal-01354578 , version 1 (18-08-2016)

Identifiants

Citer

Khalid Benabdeslem, Mohammed Hindawi. Constrained Laplacian Score for semi-supervised feature selection. ECML/PKDD. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2011, Athens, Greece. pp.204-218, ⟨10.1007/978-3-642-23780-5_23⟩. ⟨hal-01354578⟩
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