Deep Closed-Form Subspace Clustering

TL;DR

Deep Closed-Form Subspace Clustering (DCFSC) introduces a simple, parameter-free model that leverages a closed-form auto-encoder for efficient large-scale subspace clustering without complex optimization.

Contribution

The paper presents DCFSC, a novel subspace clustering method that eliminates parameters and complex training, simplifying the process and improving scalability on large, high-dimensional datasets.

Findings

Effective on large-scale datasets

No parameters needed for self-expressive layer

Significant memory efficiency

Abstract

We propose Deep Closed-Form Subspace Clustering (DCFSC), a new embarrassingly simple model for subspace clustering with learning non-linear mapping. Compared with the previous deep subspace clustering (DSC) techniques, our DCFSC does not have any parameters at all for the self-expressive layer. Instead, DCFSC utilizes the implicit data-driven self-expressive layer derived from closed-form shallow auto-encoder. Moreover, DCFSC also has no complicated optimization scheme, unlike the other subspace clustering methods. With its extreme simplicity, DCFSC has significant memory-related benefits over the existing DSC method, especially on the large dataset. Several experiments showed that our DCFSC model had enough potential to be a new reference model for subspace clustering on large-scale high-dimensional dataset.