Unsupervised feature extraction using a learned graph with clustering structure

Feature extraction, one kind of dimensionality reduction methodology, has aroused considerable research interests during the last few decades. Traditional graph embedding methods construct a fixed graph with original data to fulfill the aim of feature extraction. The lack of the graph learning mechanism leaves room for the improvement of their performances. In this paper, we propose a novel framework, termed as unsupervised feature extraction using a learned graph with clustering structure (LGCS), in which a graph learning mechanism has been presented. To be specific, the proposed LGCS learns both a transformation matrix and an ideal structured graph which incorporates clustering information. To show the effectiveness of the framework, we present a concrete method within our framework, and an iteration algorithm has been designed to solve the corresponding optimizing problem. Promising experimental results on real-world datasets have validated the effectiveness of our proposed algorithm.