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cloud.google.com·8h ago
AI Research
arXiv CS.AI·24d ago·5 min read
Randomized SVD Approximations for Spectral Co-Clustering of Word-Document Matrices
TL;DR
Two randomized methods for spectral co-clustering of word-document matrices are introduced, significantly reducing computation time compared to traditional singular value decomposition (SVD) methods. The first method uses random projection, while the second employs partial SVD with random sampling, each showing varying effectiveness based on matrix density.
✦ Why It Matters
Choose the appropriate randomized SVD method based on your dataset's density to optimize clustering performance.
Key Takeaways
- Use randomized SVD to speed up spectral co-clustering tasks.
- Select random projection for sparser datasets for better reliability.
- Employ partial SVD with sampling for denser matrices.
- Evaluate matrix sparsity before choosing a clustering method.
How It Works
The first method leverages random projection to approximate SVD, which reduces the dimensionality of the data while preserving essential features. The second method applies partial SVD combined with random sampling of matrix elements, allowing for faster computations by focusing on a subset of the data.