Deeply Transformed Subspace Clustering

Jyoti Maggu (1) , Angshul Majumdar (1) , Emilie Chouzenoux (2) , Giovanni Chierchia (3, 4)

1 IIIT-Delhi - Indraprastha Institute of Information Technology [New Delhi]
2 OPIS - OPtimisation Imagerie et Santé
3 ESIEE Paris
4 LIGM - Laboratoire d'Informatique Gaspard-Monge

Subspace clustering assumes that the data is separable into separate subspaces; this assumption may not always hold. For such cases, we assume that, even if the raw data is not separable into subspaces, one can learn a deep representation such that the learnt representation is separable into subspaces. To achieve the intended goal, we embed subspace clustering techniques (locally linear manifold clustering, sparse subspace clustering and low rank representation) into deep transform learning. The entire formulation is jointly learnt; giving rise to a new class of methods called deeply transformed subspace clustering (DTSC). To test the performance of the proposed techniques, benchmarking is performed on image clustering problems. Comparison with state-of-the-art clustering techniques shows that our formulation improves upon them.

Domaines: Traitement du signal et de l'image [eess.SP]

Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia. Deeply Transformed Subspace Clustering. Signal Processing, 2020, 174, pp.107628. ⟨10.1016/j.sigpro.2020.107628⟩. ⟨hal-03066160⟩