Binary Label Learning for Semi-Supervised Feature Selection

doi:10.1109/TKDE.2021.3109243

Binary Label Learning for Semi-Supervised Feature Selection

2021-01-01

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DOI：10.1109/TKDE.2021.3109243

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Abstract

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Semi-supervised feature selection methods jointly exploit the labelled and unlabelled samples when selecting the features. Under the semi-supervised learning scenario, the number of labelled data significantly impacts the feature selection performance. In this paper, we introduce the label learning with binary hashing to the research field of feature selection and propose a novel Semi-supervised Feature Selection with Binary Label Learning (SFS-BLL) model. Specifically, we learn the binary hash codes as the pseudo labels by specially imposing binary hash constraints on the spectral embedding process to increase the number of labels. Meanwhile, we propose a self-weighted sparse regression module which exploits the learned labels and given manual labels together with importance differentiation to guide the feature selection process. Finally, we develop an effective discrete optimization method based on the Alternating Direction Method of Multipliers (ADMM) to iteratively optimize the binary labels and the feature selection matrix. Extensive experiments on widely tested benchmarks demonstrate the superiority of the proposed method from various aspects.

Keywords:

Feature extraction

Manuals

Semantics

Training

Data models

Convex functions

Computer science

Semi-supervised learning

feature selection

binary hashing

spectral embedding

importance differentiation

Binary Label Learning for Semi-Supervised Feature Selection

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