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- W4377121455 abstract "Graph Signal Filter used as dimensionality reduction in spectral clustering usually requires expensive eigenvalue estimation. We analyze the filter in an optimization setting and propose to use four orthogonalization-free methods by optimizing objective functions as dimensionality reduction in spectral clustering. The proposed methods do not utilize any orthogonalization, which is known as not well scalable in a parallel computing environment. Our methods theoretically construct adequate feature space, which is, at most, a weighted alteration to the eigenspace of a normalized Laplacian matrix. We numerically hypothesize that the proposed methods are equivalent in clustering quality to the ideal Graph Signal Filter, which exploits the exact eigenvalue needed without expensive eigenvalue estimation. Numerical results show that the proposed methods outperform Power Iteration-based methods and Graph Signal Filter in clustering quality and computation cost. Unlike Power Iteration-based methods and Graph Signal Filter which require random signal input, our methods are able to utilize available initialization in the streaming graph scenarios. Additionally, numerical results show that our methods outperform ARPACK and are faster than LOBPCG in the streaming graph scenarios. We also present numerical results showing the scalability of our methods in multithreading and multiprocessing implementations to facilitate parallel spectral clustering." @default.
- W4377121455 created "2023-05-20" @default.
- W4377121455 creator A5047703518 @default.
- W4377121455 creator A5079602544 @default.
- W4377121455 date "2023-05-16" @default.
- W4377121455 modified "2023-09-29" @default.
- W4377121455 title "Spectral Clustering via Orthogonalization-Free Methods" @default.
- W4377121455 doi "https://doi.org/10.48550/arxiv.2305.10356" @default.
- W4377121455 hasPublicationYear "2023" @default.
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