Unsupervised Deep Discriminant Analysis Based Clustering

TL;DR

This paper introduces an unsupervised deep discriminant analysis method that leverages neural networks to improve clustering by enhancing inter-cluster differences and reducing intra-cluster variance, applicable to various data types.

Contribution

It proposes a novel deep learning-based clustering approach that optimally separates data clusters in a nonlinear latent space, with an extension to incorporate graph information for better performance.

Findings

Effective clustering on image and non-image data

Improved performance with graph information integration

Demonstrated superiority over existing methods

Abstract

This work presents an unsupervised deep discriminant analysis for clustering. The method is based on deep neural networks and aims to minimize the intra-cluster discrepancy and maximize the inter-cluster discrepancy in an unsupervised manner. The method is able to project the data into a nonlinear low-dimensional latent space with compact and distinct distribution patterns such that the data clusters can be effectively identified. We further provide an extension of the method such that available graph information can be effectively exploited to improve the clustering performance. Extensive numerical results on image and non-image data with or without graph information demonstrate the effectiveness of the proposed methods.