Chapter 5

From Bi-Level Sparse Clustering to Deep Clustering

Zhangyang Wang    Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States

Abstract

Many clustering methods highly depend on extracted features. We propose a joint optimization framework in terms of both feature extraction and discriminative clustering. We utilize graph regularized sparse codes as the features, and formulate sparse coding as the constraint for clustering. Two cost functions are developed based on entropy-minimization and maximum-margin clustering principles, respectively. They are considered as the objectives to be minimized. Solving such a bi-level optimization mutually reinforces both sparse coding and clustering ...

Get Deep Learning through Sparse and Low-Rank Modeling now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.