Randomized SVD Approximations for Spectral Co-Clustering

VERA Brief

AI-generated. Grounded in the article and its cited sources.

Researchers have developed two randomized approximation methods for spectral co-clustering of word-document matrices. These methods aim to improve efficiency on high-dimensional data by using randomized SVD techniques to reduce computational costs.

Key facts

• Two randomized approximation methods for normalized spectral co-clustering of bipartite text data have been detailed.
• Spectral co-clustering is used to find latent structures in word-document matrices.
• Traditional singular value decomposition can be computationally intensive for high-dimensional datasets.
• The new methods involve randomized SVD through random projection and a combination of partial SVD with element-wise random sampling.
• Both methods decreased runtime compared to a full-SVD baseline, with effectiveness dependent on matrix sparsity.