scholarly journals Locality-Sensitive Hashing Techniques for Nearest Neighbor Search

2012 ◽  
Vol 12 (4) ◽  
pp. 300-307 ◽  
Author(s):  
Keon Myung Lee
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Locality Sensitive Hashing ◽  
Neighbor Search

Fast document summarization using locality sensitive hashing and memory access efficient node ranking

2016 ◽  
Vol 6 (3) ◽  
pp. 945
Author(s):  
Ercan Canhasi
Keyword(s):  
Time Complexity ◽  
Nearest Neighbor ◽  
Linear Time ◽  
Nearest Neighbor Search ◽  
Memory Access ◽  
Locality Sensitive Hashing ◽  
Document Summarization ◽  
Neighbor Search ◽  
Node Ranking ◽  
Similarity Graph

Text modeling and sentence selection are the fundamental steps of a typical extractive document summarization algorithm.   The common text modeling method connects a pair of sentences based on their similarities.   Even thought it can effectively represent the sentence similarity graph of given document(s) its big drawback is a large time complexity of $O(n^2)$, where n represents the number of sentences.   The quadratic time complexity makes it impractical for large documents.   In this paper we propose the fast approximation algorithms for the text modeling and the sentence selection.   Our text modeling algorithm reduces the time complexity to near-linear time by rapidly finding the most similar sentences to form the sentences similarity graph.   In doing so we utilized Locality-Sensitive Hashing, a fast algorithm for the approximate nearest neighbor search.   For the sentence selection step we propose a simple memory-access-efficient node ranking method based on the idea of scanning sequentially only the neighborhood arrays.    Experimentally, we show that sacrificing a rather small percentage of recall and precision in the quality of the produced summary can reduce the quadratic to sub-linear time complexity.   We see the big potential of proposed method in text summarization for mobile devices and big text data summarization for internet of things on cloud.   In our experiments, beside evaluating the presented method on the standard general and query multi-document summarization tasks, we also tested it on few alternative summarization tasks including general and query, timeline, and comparative summarization.

scholarly journals A Probabilistic Molecular Fingerprint for Big Data Settings

2018 ◽  
Author(s):  
Daniel Probst ◽  
Jean-Louis Reymond
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Locality Sensitive Hashing ◽  
Molecular Fingerprint ◽  
Molecular Fingerprints ◽  
Approximate Nearest Neighbor ◽  
Neighbor Search ◽  
Large Databases ◽  
Nearest Neighbor Searches ◽  
Extended Connectivity

<p><b>Background</b>: Among the various molecular fingerprints available to describe small organic molecules, ECFP4 (extended connectivity fingerprint, up to four bonds) performs best in benchmarking drug analog recovery studies as it encodes substructures with a high level of detail. Unfortunately, ECFP4 requires high dimensional representations (≥1,024D) to perform well, resulting in ECFP4 nearest neighbor searches in very large databases such as GDB, PubChem or ZINC to perform very slowly due to the curse of dimensionality. <a></a><a></a></p> <p><b>Results</b>: Herein we report a new fingerprint, called MHFP6 (MinHash fingerprint, up to six bonds), which encodes detailed substructures using the extended connectivity principle of ECFP in a fundamentally different manner, increasing the performance of exact nearest neighbor searches in benchmarking studies and enabling the application of locality sensitive hashing (LSH) approximate nearest neighbor search algorithms. To describe a molecule, MHFP6 extracts the SMILES of all circular substructures around each atom up to a diameter of six bonds and applies the MinHash method to the resulting set. MHFP6 outperforms ECFP4 in benchmarking analog recovery studies. Furthermore, MHFP6 outperforms ECFP4 in approximate nearest neighbor searches by two orders of magnitude in terms of speed, while decreasing the error rate. </p> <p><b>Conclusion</b><a></a><a>: MHFP6 is a new molecular fingerprint, encoding circular substructures, which outperforms ECFP4 for analog searches while allowing the direct application of locality sensitive hashing algorithms. It should be well suited for the analysis of large databases. The source code for MHFP6 is available on GitHub (</a><a href="https://github.com/reymond-group/mhfp">https://github.com/reymond-group/mhfp</a>).<a></a></p>

scholarly journals Stochastically Robust Personalized Ranking for LSH Recommendation Retrieval

2020 ◽  
Vol 34 (04) ◽  
pp. 4594-4601
Author(s):  
Dung D. Le ◽  
Hady W. Lauw
Keyword(s):  
Nearest Neighbor ◽  
Hash Functions ◽  
Nearest Neighbor Search ◽  
Locality Sensitive Hashing ◽  
Neighbor Search ◽  
Data Points ◽  
Recommendation Accuracy ◽  
Prohibitive Cost ◽  
The Cost

Locality Sensitive Hashing (LSH) has become one of the most commonly used approximate nearest neighbor search techniques to avoid the prohibitive cost of scanning through all data points. For recommender systems, LSH achieves efficient recommendation retrieval by encoding user and item vectors into binary hash codes, reducing the cost of exhaustively examining all the item vectors to identify the top-k items. However, conventional matrix factorization models may suffer from performance degeneration caused by randomly-drawn LSH hash functions, directly affecting the ultimate quality of the recommendations. In this paper, we propose a framework named øurmodel, which factors in the stochasticity of LSH hash functions when learning real-valued user and item latent vectors, eventually improving the recommendation accuracy after LSH indexing. Experiments on publicly available datasets show that the proposed framework not only effectively learns user's preferences for prediction, but also achieves high compatibility with LSH stochasticity, producing superior post-LSH indexing performances as compared to state-of-the-art baselines.

scholarly journals A Probabilistic Molecular Fingerprint for Big Data Settings

2018 ◽  
Author(s):  
Daniel Probst ◽  
Jean-Louis Reymond
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Locality Sensitive Hashing ◽  
Molecular Fingerprint ◽  
Molecular Fingerprints ◽  
Approximate Nearest Neighbor ◽  
Neighbor Search ◽  
Large Databases ◽  
Nearest Neighbor Searches ◽  
Extended Connectivity

<p><b>Background</b>: Among the various molecular fingerprints available to describe small organic molecules, ECFP4 (extended connectivity fingerprint, up to four bonds) performs best in benchmarking drug analog recovery studies as it encodes substructures with a high level of detail. Unfortunately, ECFP4 requires high dimensional representations (≥1,024D) to perform well, resulting in ECFP4 nearest neighbor searches in very large databases such as GDB, PubChem or ZINC to perform very slowly due to the curse of dimensionality. <a></a><a></a></p> <p><b>Results</b>: Herein we report a new fingerprint, called MHFP6 (MinHash fingerprint, up to six bonds), which encodes detailed substructures using the extended connectivity principle of ECFP in a fundamentally different manner, increasing the performance of exact nearest neighbor searches in benchmarking studies and enabling the application of locality sensitive hashing (LSH) approximate nearest neighbor search algorithms. To describe a molecule, MHFP6 extracts the SMILES of all circular substructures around each atom up to a diameter of six bonds and applies the MinHash method to the resulting set. MHFP6 outperforms ECFP4 in benchmarking analog recovery studies. Furthermore, MHFP6 outperforms ECFP4 in approximate nearest neighbor searches by two orders of magnitude in terms of speed, while decreasing the error rate. </p> <p><b>Conclusion</b><a></a><a>: MHFP6 is a new molecular fingerprint, encoding circular substructures, which outperforms ECFP4 for analog searches while allowing the direct application of locality sensitive hashing algorithms. It should be well suited for the analysis of large databases. The source code for MHFP6 is available on GitHub (</a><a href="https://github.com/reymond-group/mhfp">https://github.com/reymond-group/mhfp</a>).<a></a></p>

scholarly journals Fast document summarization using locality sensitive hashing and memory access efficient node ranking

2016 ◽  
Vol 6 (3) ◽  
pp. 945
Author(s):  
Ercan Canhasi
Keyword(s):  
Time Complexity ◽  
Nearest Neighbor ◽  
Linear Time ◽  
Nearest Neighbor Search ◽  
Memory Access ◽  
Locality Sensitive Hashing ◽  
Document Summarization ◽  
Neighbor Search ◽  
Node Ranking ◽  
Similarity Graph

Text modeling and sentence selection are the fundamental steps of a typical extractive document summarization algorithm.   The common text modeling method connects a pair of sentences based on their similarities.   Even thought it can effectively represent the sentence similarity graph of given document(s) its big drawback is a large time complexity of $O(n^2)$, where n represents the number of sentences.   The quadratic time complexity makes it impractical for large documents.   In this paper we propose the fast approximation algorithms for the text modeling and the sentence selection.   Our text modeling algorithm reduces the time complexity to near-linear time by rapidly finding the most similar sentences to form the sentences similarity graph.   In doing so we utilized Locality-Sensitive Hashing, a fast algorithm for the approximate nearest neighbor search.   For the sentence selection step we propose a simple memory-access-efficient node ranking method based on the idea of scanning sequentially only the neighborhood arrays.    Experimentally, we show that sacrificing a rather small percentage of recall and precision in the quality of the produced summary can reduce the quadratic to sub-linear time complexity.   We see the big potential of proposed method in text summarization for mobile devices and big text data summarization for internet of things on cloud.   In our experiments, beside evaluating the presented method on the standard general and query multi-document summarization tasks, we also tested it on few alternative summarization tasks including general and query, timeline, and comparative summarization.

MP-RW-LSH

2021 ◽  
Vol 14 (13) ◽  
pp. 3267-3280
Author(s):  
Huayi Wang ◽  
Jingfan Meng ◽  
Long Gong ◽  
Jun Xu ◽  
Mitsunori Ogihara
Keyword(s):  
Nearest Neighbor ◽  
Edit Distance ◽  
State Of The Art ◽  
Hash Table ◽  
Nearest Neighbor Search ◽  
Locality Sensitive Hashing ◽  
Algorithmic Problem ◽  
Use Case ◽  
Hash Tables ◽  
Neighbor Search

Approximate Nearest Neighbor Search (ANNS) is a fundamental algorithmic problem, with numerous applications in many areas of computer science. Locality-Sensitive Hashing (LSH) is one of the most popular solution approaches for ANNS. A common shortcoming of many LSH schemes is that since they probe only a single bucket in a hash table, they need to use a large number of hash tables to achieve a high query accuracy. For ANNS- L 2 , a multi-probe scheme was proposed to overcome this drawback by strategically probing multiple buckets in a hash table. In this work, we propose MP-RW-LSH, the first and so far only multi-probe LSH solution to ANNS in L 1 distance, and show that it achieves a better tradeoff between scalability and query efficiency than all existing LSH-based solutions. We also explain why a state-of-the-art ANNS -L 1 solution called Cauchy projection LSH (CP-LSH) is fundamentally not suitable for multi-probe extension. Finally, as a use case, we construct, using MP-RW-LSH as the underlying "ANNS- L 1 engine", a new ANNS-E (E for edit distance) solution that beats the state of the art.

Speeding up probabilistic roadmap planners with locality-sensitive hashing

Robotica ◽  
2014 ◽  
Vol 33 (7) ◽  
pp. 1491-1506
Author(s):  
Mika T. Rantanen ◽  
Martti Juhola
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Locality Sensitive Hashing ◽  
Exact Methods ◽  
Neighbor Search ◽  
Probabilistic Roadmap ◽  
Nearest Neighbor Searching ◽  
Major Bottleneck ◽  
Speed Up

SUMMARYA crucial part of probabilistic roadmap planners is the nearest neighbor search, which is typically done by exact methods. Unfortunately, searching the neighbors can become a major bottleneck for the performance. This can occur when the roadmap size grows especially in high-dimensional spaces. In this paper, we investigate how well the approximate nearest neighbor searching works with probabilistic roadmap planners. We propose a method that is based on the locality-sensitive hashing and show that it can speed up the construction of the roadmap considerably without reducing the quality of the produced roadmap.

Bi-Level Locality Sensitive Hashing Index Based on Clustering

2014 ◽  
Vol 556-562 ◽  
pp. 3804-3808
Author(s):  
Peng Wang ◽  
Dong Yin ◽  
Tao Sun
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Locality Sensitive Hashing ◽  
Search Performance ◽  
Hash Tables ◽  
Approximate Nearest Neighbor Search ◽  
Approximate Nearest Neighbor ◽  
Neighbor Search ◽  
Search Speed ◽  
Locality Sensitive Hash

Locality sensitive hashing is the most popular algorithm for approximate nearest neighbor search. As LSH partitions vector space uniformly and the distribution of vectors is usually non-uniform, it poorly fits real dataset and has limited search performance. In this paper, we propose a new Bi-level locality sensitive hashing algorithm, which has two-level structures to perform approximate nearest neighbor search in high dimensional spaces. In the first level, we train a number of cluster centers, then use the cluster centers to divide the dataset into many clusters and the vectors in each cluster has near uniform distribution. In the second level, we construct locality sensitive hashing tables for each cluster. Given a query, we determine a few clusters that it belongs to with high probability, and then perform approximate nearest neighbor search in the corresponding locality sensitive hash tables. Experimental results on the dataset of 1,000,000 vectors show that the search speed can be increased by 48 times compared to Euclidean locality sensitive hashing, while keeping high search precision.

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