期刊
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
卷 8, 期 3, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2987378
关键词
Non-negative matrix factorization; manifold learning; feature learning; clustering
资金
- National Science Foundation [IIS-1218712]
- National Natural Science Foundation of China [11241005]
- Shanxi Scholarship Council of China [2015-093]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1218712] Funding Source: National Science Foundation
Matrix factorization is a useful technique for data representation in many data mining andmachine learning tasks. Particularly, for data sets with all nonnegative entries, matrix factorization often requires that factor matrices be nonnegative, leading to nonnegative matrix factorization (NMF). One important application of NMF is for clustering with reduced dimensions of the data represented in the new feature space. In this paper, we propose a new graph regularized NMF method capable of feature learning and apply it to clustering. Unlike existing NMF methods that treat all features in the original feature space equally, our method distinguishes features by incorporating a feature-wise sparse approximation error matrix in the formulation. It enables important features to be more closely approximated by the factor matrices. Meanwhile, the graph of the data is constructed using cleaner features in the feature learning process, which integrates feature learning andmanifold learning procedures into a unified NMF model. This distinctly differs from applying the existing graph-based NMF models after feature selection in that, when these two procedures are independently used, they often fail to align themselves toward obtaining a compact and most expressive data representation. Comprehensive experimental results demonstrate the effectiveness of the proposed method, which outperforms state-of-the-art algorithms when applied to clustering.
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