Improved Consistent Sampling, Weighted Minhash and L1 Sketching

By Sergey Ioffe. 2010

235 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Algorithms & Complexity

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Sanjiv Kumar
2. Wei David Liu
3. Luc Van Gool
4. Shih-Fu Chang
5. Dacheng Tao
6. Anshumali Shrivastava
7. Chunhua Shen
8. Xiaogang Wang

In Algorithms & Complexity

1. Ping Li
2. Anshumali Shrivastava
3. Bernhard Haeupler
4. Mark S. Manasse
5. Kunal Talwar
6. Sanjiv Kumar
7. Wei David Liu
8. Jun Wang

Citations in the corpus

• Beyond Pairwise: Provably Fast Algorithms for Approximate k-Way Similarity Search (2013, result): “For example, our results naturally apply to consistent weighted sampling [25, 15], which is one way to handle non-binary sparse data.”
• BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models (2021, methodology): “To measure the domain similarity between the datasets, we compute a pairwise weighted Jaccard similarity (Ioffe, 2010) on unigram word overlap between the document collections.”
• Asymmetric Minwise Hashing (2014, methodology): “At this point, we can use existing methods for consistent weighted sampling on the new data after asymmetric transformations [29, 22, 18].”
• Min-Max Kernels (2015, methodology): “The prior efforts [24, 14] have lead to the method called “consistent weighted sampling (CWS)”for hashing min-max kernels.”
• Asymmetric Minwise Hashing for Indexing Binary Inner Products and Set Containment (2015, methodology): “At this point, we can use existing methods for consistent weighted sampling [30, 23, 19, 26].”
• Sequence Synopsis: Optimize Visual Summary of Temporal Event Data (2018, methodology): “Based on this observation, we integrate weighted LSH [16] into the MinDL algorithm.”
• Single cell RNA-seq data clustering using TF-IDF based methods (2018, methodology): “[29] [30] techniques that make feasible the efficient computation of highly similar”
• Consistent Weighted Sampling Made Fast, Small, and Easy (2014, background): “More recent papers [12, 19, 14] removed the integer weight restriction, and showed how to produce samples using a constant number of hash evaluations for any element, independent of its weight.”
• Learning to Hash for Indexing Big Data—A Survey (2015, methodology): “By converting rank lists to a triplet tensor matrix, listwise hashing is designed to preserve the ranking in the Hamming space [76].”