Approximating matrix multiplication for pattern recognition tasks

By Edith Cohen, David D. Lewis. 1997

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In Algorithms & Complexity

Citations in the corpus

Quantization based Fast Inner Product Search (2015, background): “[6] studied it in the context of document clustering and presented a method based on randomized sampling without computing the full matrix-vector multiplication.”
Efficient set joins on similarity predicates (2004, methodology): “Approximate methods make it easy to apply various sampling (as in [8] and [12]) and signature techniques (like MinHash in [4, 7, 3]).”
Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication (2006, background): “To the best of our knowledge, the only previous randomized algorithm that approximates the product of two matrices is that of Cohen and Lewis [7].”
Sampling-bias-corrected neural modeling for large corpus item recommendations (2019, background): “, [2, 10, 25], for approximate maximum inner product search (MIPS) problems.”
Factorized Deep Retrieval and Distributed TensorFlow Serving (2018, unknown): “Low-latency online retrieval is based on a highly efficient similarity search system built on hashing techniques, e.g., [2, 4, 7, 9], for approximate maximum inner product search (MIPS) problems.”
A new algorithm for optimal 2-constraint satisfaction and its implications (2005, unknown): “[8]).”
Self-supervised Learning for Large-scale Item Recommendations (2020, methodology): “These factorized model architectures make it possible to convert the problem of finding top-k items to Maximum-InnerProduct-Search (MIPS) [10], solvable in sublinear complexity.”
Review of "Mining the Web: discovering knowledge from hypertext data" by Soumen Chakrabati. Morgan Kaufman 2003. (2002, unknown): “An elegant algorithm for bulk-queries is given by Cohen and Lewis [52].”
Improved Approximation Algorithms for Large Matrices via Random Projections (2006, background): “Cohen and Lewis set up random walks to approximate non-negative matrix products [15].”