Discriminative clustering using regularized subspace learning. (December 2019) - Passalis, Nikolaos
Highlights: A regularized subspace learning method for discriminative clustering is proposed. A similarity-based subspace learning formulation is used for the optimization. Robust representations optimized for clustering tasks are learned. The proposed method is extensively evaluated using four datasets. Abstract: Clustering is among the most important unsupervised learning tasks with several applications in a wide range of domains. Discriminative Clustering (DC) techniques combine the unsupervised nature of clustering with the high discriminative ability of supervised subspace learning methods by simultaneously performing clustering and learning a representation that encourages the separability of the clusters. However, in contrast with classical supervised learning tasks, where the labels are usually correct, cluster assignments are inherently noisy leading to suboptimal results when used as ground truth information with highly discriminative methods. To this end, a novel similarity-based subspace learning method, that allows for learning regularized clustering-oriented representations, avoiding the pitfalls of highly discriminative methods, such as Linear Discriminant Analysis (LDA), is proposed in this paper. The ability of the proposed method to improve the quality of the obtained clustering solutions is demonstrated using extensive experiments on four datasets.