Neural Collaborative Subspace Clustering
ICML 2019 | We introduce 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 models, ours runs without the aid of spectral clustering. This enables our algorithm to gracefully scale to large datasets. At its heart, our neural model benefits from a classifier that 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 one based on a notion of subspace self-expressiveness, to supervise training in a collaborative scheme.
Collaborators: Tong Zhang, Pan Ji, Mehrtash Harandi, Wenbing Huang, Hongdong Li