Clustering-aware graph construction: A joint learning perspective
Author(s)
Author(s)
Jia, Y.
Hou, J.
Kwong, S.
Date Issued
2020
Publisher
IEEE
Journal
IEEE Transactions on Signal and Information Processing over Networks
Volume
6
Start page
357
End page
370
Abstract
Graph-based clustering methods have demonstrated the effectiveness in various applications. Generally, existing graph-based clustering methods first construct a graph to represent the input data and then partition it to generate the clustering result. However, such a stepwise manner may make the constructed graph not fit the requirements for the subsequent decomposition, leading to compromised clustering accuracy. To this end, we propose a joint learning framework, which is able to learn the graph and the clustering result simultaneously, such that the resulting graph is tailored to the clustering task. The proposed method is formulated as a well-defined nonnegative and off-diagonal constrained optimization problem,which is optimized by an alternative iteration method with the convergence of the value of the objective function guaranteed. The advantage of the proposed model is demonstrated by comparing with 19 state-of-the-art clustering methods on 10 datasets with 4 clustering metrics.
SFU Affiliated Publication
No
Availability at SFU Library
No database links found.