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.
|Number of pages||14|
|Journal||IEEE Transactions on Signal and Information Processing over Networks|
|Early online date||20 Apr 2020|
|Publication status||Published - 2020|
Bibliographical noteFunding Information:
This work was supported in part by the Natural Science Foundation of China under Grants 61871342, 61772344, and 61672443, in part by the Hong Kong RGC General Research Funds under Grants 9042820 (CityU 11219019), 9042489 (CityU 11206317), 9042322 (CityU 11200116), 9042816 (CityU 11209819), and 9048123 (CityU 21211518), and in part by the Key Project of Science and Technology Innovation 2030 supported by the Ministry of Science and Technology of China under Grant 2018AAA0101301.
© 2015 IEEE.
- Adaptive graph learning