Efficient method for symmetric nonnegative matrix factorization with an approximate augmented Lagrangian scheme
Author:
Funder
National Natural Science Foundation of China
Jiangsu University Foundation
Publisher
Elsevier BV
Reference32 articles.
1. Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values;Paatero;Environmetrics,1994
2. An alternating rank-k nonnegative least squares framework (arknls) for nonnegative matrix factorization;Chu;SIAM J. Matrix Anal. Appl.,2021
3. Structured sparse non-negative matrix factorization with ℓ2,0-norm;Min;IEEE Trans. Knowl. Data Eng.,2022
4. Learning the parts of objects by non-negative matrix factorization;Lee;Nature,1999
5. C. Ding, T. Li, W. Peng, H. Park, Orthogonal nonnegative matrix t-factorizations for clustering, in: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006, pp. 126–135.
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