Joint Spectral Clustering based on Optimal Graph and Feature Selection

Redundant features and outliers (noise) included in the data points for a machine learning clustering model heavily influences the discovery of more distinguished features for clustering. To solve this issue, we propose a spectral new clustering method to consider the feature selection with the L2 , 1-norm regularization as well as simultaneously learns orthogonal representations for each sample to preserve the local structures of data points. Our model also solves the issue of out-of-sample, where the training process does not output an explicit model to predict unseen data points, along with providing an efficient optimization method for the proposed objective function. Experimental results showed that our method on twelve data sets achieves the best performance compared with other similar models.

Description

CAUL read and publish agreement 2022

Keywords

Feature selection, Clustering, Graph matrix, dimensionality reduction, subspace learning

Citation

NEURAL PROCESSING LETTERS, 2021, 53 (1), pp. 257 - 273

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