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Randomized SVD Approximations for Spectral Co-Clustering of Word-Document Matrices
spectral clusteringrandomized algorithmsmatrix decomposition
2609.19243
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2h ago70%
Abstract
This paper presents two randomized approximations for normalized spectral co-clustering of bipartite text data, improving runtime efficiency over traditional methods.
Reality Card
Core Claim
The paper demonstrates that randomized approximations for spectral co-clustering can significantly reduce runtime compared to full-SVD methods, with performance varying based on matrix sparsity.
Method / Result
Both randomized methods reduce runtime relative to the full-SVD baseline, with the random projection method being more reliable across various settings.
Limitations
The effectiveness of the methods is dependent on the sparsity of the matrices, which may limit generalizability.
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