Projects | Scalable Deep K-Subspace Clustering

ACCV 2018 | We introduce a method that simultaneously learns an embedding space along with subspaces within it to minimize a notion of reconstruction error. This addresses the problem of subspace clustering in an end-to-end learning paradigm. To achieve our goal, we propose a scheme to update subspaces within a deep neural network. This, in turn, frees us from the need of having an affinity matrix to perform clustering. Unlike previous attempts, our method can easily scale up to large datasets, making it unique in the context of unsupervised learning with deep architectures.