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Convolutional Subspace Clustering Network with Block Diagonal Prior
Standard methods of subspace clustering are based on self-expressiveness in the original data space, which states that a data point in a subspace can be expressed as a linear combination of other points.
Self-Supervised Convolutional Subspace Clustering Network
However, the applicability of subspace clustering has been limited because practical visual data in raw form do not necessarily lie in such linear subspaces.
Ranked #2 on
Image Clustering
on Extended Yale-B
Constrained Sparse Subspace Clustering with Side-Information
Subspace clustering refers to the problem of segmenting high dimensional data drawn from a union of subspaces into the respective subspaces.
