Unsupervised Spectral Feature Selection With Dynamic Hyper-Graph Learning

Xiaofeng Zhu

doi:10.1109/TKDE.2020.3017250

Unsupervised Spectral Feature Selection With Dynamic Hyper-Graph Learning

2020-01-01

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DOI：10.1109/TKDE.2020.3017250

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Unsupervised spectral feature selection (USFS) methods could output interpretable and discriminative results by embedding a Laplacian regularizer in the framework of sparse feature selection to keep the local similarity of the training samples. To do this, USFS methods usually construct the Laplacian matrix using either a general-graph or a hyper-graph on the original data. Usually, a general-graph could measure the relationship between two samples while a hyper-graph could measure the relationship among no less than two samples. Obviously, the general-graph is a special case of the hyper-graph and the hyper-graph may capture more complex structure of samples than the general graph. However, in previous USFS methods, the construction of the Laplacian matrix is separated from the process of feature selection. Moreover, the original data usually contain noise. Each of them makes difficult to output reliable feature selection models. In this paper, we propose a novel feature selection method by dynamically constructing a hyper-graph based Laplacian matrix in the framework of sparse feature selection. Experimental results on real datasets showed that our proposed method outperformed the state-of-the-art methods in terms of both clustering and segmentation tasks.

Keywords:

Feature extraction

Laplace equations

Training

Data models

Sparse matrices

Dimensionality reduction

Covariance matrices

Feature selection

hyper-graph

segmentation

subspace learning

dimensionality reduction

Unsupervised Spectral Feature Selection With Dynamic Hyper-Graph Learning

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