Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105685
Title: | Adaptive manifold regularized matrix factorization for data clustering | Authors: | Zhang, L Zhang, Q Du, B You, J Tao, D |
Issue Date: | 2017 | Source: | Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia, 19-25 August 2017, p. 3399-3405 | Abstract: | Data clustering is the task to group the data samples into certain clusters based on the relationships of samples and structures hidden in data, and it is a fundamental and important topic in data mining and machine learning areas. In the literature, the spectral clustering is one of the most popular approaches and has many variants in recent years. However, the performance of spectral clustering is determined by the affinity matrix, which is always computed by a predefined model (e.g., Gaussian kernel function) with carefully tuned parameters combination, and may far from optimal in practice. In this paper, we propose to consider the observed data clustering as a robust matrix factorization point of view, and learn an affinity matrix simultaneously to regularize the proposed matrix factorization. The solution of the proposed adaptive manifold regularized matrix factorization (AMRMF) is reached by a novel Augmented Lagrangian Multiplier (ALM) based algorithm. The experimental results on standard clustering datasets demonstrate the superior performance over the exist alternatives. | Publisher: | International Joint Conferences on Artificial Intelligence | ISBN: | 978-0-9992411-0-3 (Online) | DOI: | 10.24963/ijcai.2017/475 | Rights: | Posted with permission of the IJCAI Organization (https://www.ijcai.org/). The following …. |
Appears in Collections: | Conference Paper |
Show full item record
Page views
8
Citations as of Apr 28, 2024
SCOPUSTM
Citations
48
Citations as of Apr 19, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.