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Clustering by Nonnegative Matrix Factorization Using Graph Random Walk
Zhirong Yang · Tele Hao · Onur Dikmen · Xi Chen · Erkki Oja

Mon Dec 03 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor #None

Nonnegative Matrix Factorization (NMF) is a promising relaxation technique for clustering analysis. However, conventional NMF methods that directly approximate the pairwise similarities using the least square error often yield mediocre performance for data in curved manifolds because they can capture only the immediate similarities between data samples. Here we propose a new NMF clustering method which replaces the approximated matrix with its smoothed version using random walk. Our method can thus accommodate farther relationships between data samples. Furthermore, we introduce a novel regularization in the proposed objective function in order to improve over spectral clustering. The new learning objective is optimized by a multiplicative Majorization-Minimization algorithm with a scalable implementation for learning the factorizing matrix. Extensive experimental results on real-world datasets show that our method has strong performance in terms of cluster purity.

Author Information

Zhirong Yang (Aalto University)
Hotloo Hao (The Curious AI Company)
Onur Dikmen (University of Helsinki)
Xi Chen (NYU)
Erkki Oja (Aalto University)

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