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

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

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

排序理由 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]

在 arXiv cs.AI 阅读 →

• Fateme Mazdarani

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