Neural Collaborative Subspace Clustering
This addresses clustering challenges in high-dimensional data analysis, offering a scalable alternative to spectral methods, but appears incremental as it builds on existing subspace clustering approaches.
The paper tackles the problem of clustering data points from multiple low-dimensional subspaces by introducing Neural Collaborative Subspace Clustering, which eliminates the need for spectral clustering and scales well to large datasets. The model achieves competitive performance compared to state-of-the-art clustering algorithms, though no specific numerical results are provided in the abstract.
We introduce the Neural Collaborative Subspace Clustering, a neural model that discovers clusters of data points drawn from a union of low-dimensional subspaces. In contrast to previous attempts, our model runs without the aid of spectral clustering. This makes our algorithm one of the kinds that can gracefully scale to large datasets. At its heart, our neural model benefits from a classifier which determines whether a pair of points lies on the same subspace or not. Essential to our model is the construction of two affinity matrices, one from the classifier and the other from a notion of subspace self-expressiveness, to supervise training in a collaborative scheme. We thoroughly assess and contrast the performance of our model against various state-of-the-art clustering algorithms including deep subspace-based ones.