Deep Closed-Form Subspace Clustering
This work addresses subspace clustering for large-scale high-dimensional datasets, offering a simpler and more efficient alternative to existing methods, though it appears incremental in its approach.
The paper tackled the problem of subspace clustering by proposing Deep Closed-Form Subspace Clustering (DCFSC), a parameter-free and simple model that eliminates the need for a self-expressive layer and complex optimization, resulting in significant memory benefits on large datasets.
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.