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New randomized SVD methods speed up spectral co-clustering for text data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed two randomized approximation methods for spectral co-clustering on word-document matrices, aiming to improve efficiency over traditional singular value decomposition (SVD). The first method employs randomized SVD via random projection, while the second integrates partial SVD with element-wise random sampling. Both approaches demonstrate reduced runtime on various datasets, though their effectiveness is influenced by matrix sparsity, with the random projection method generally showing more consistent results. AI

IMPACT Introduces more efficient algorithms for analyzing large text datasets, potentially improving downstream NLP applications.

RANK_REASON The cluster contains a research paper detailing new algorithmic methods for a specific machine learning task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New randomized SVD methods speed up spectral co-clustering for text data

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The cluster contains a research paper detailing new algorithmic methods for a specific machine learning task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Fateme Mazdarani, Carlos Toxtli ·

    Randomized SVD Approximations for Spectral Co-Clustering of Word-Document Matrices

    arXiv:2609.19243v1 Announce Type: cross Abstract: Spectral co-clustering is a useful tool for discovering latent structure in word-document matrices, but its reliance on singular value decomposition (SVD) can make standard formulations expensive on high-dimensional data. This pap…

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