@inproceedings{f66996d83a814dd4a57a63b42ab3807c,
title = "Joint Structured Graph Learning and Clustering Based on Concept Factorization",
abstract = "As one of the matrix factorization models, concept factorization (CF) achieved promising performance in learning data representation in both original feature space and reproducible kernel Hilbert space (RKHS). Based on the consensuses that 1) learning performance of models can be enhanced by exploiting the geometrical structure of data and 2) jointly performing structured graph learning and clustering can avoid the suboptimal solutions caused by the two-stage strategy in graph-based learning, we developed a new CF model with self-expression. Our model has a combined coefficient matrix which is able to learn more efficiently. In other words, we propose a CF-based joint structured graph learning and clustering model (JSGCF). A new efficient iterative method is developed to optimize the JSGCF objective function. Experimental results on representative data sets demonstrate the effectiveness of our new JSGCF algorithm.",
keywords = "clustering, concept factorization, joint learning, Structured graph learning",
author = "Yong Peng and Rixin Tang and Wanzeng Kong and Jianhai Zhang and Feiping Nie and Andrzej Cichocki",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",
year = "2019",
month = may,
doi = "10.1109/ICASSP.2019.8682779",
language = "英语",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3162--3166",
booktitle = "2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings",
}