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Approximate kNN Classification for Biomedical Data
[article]
2020
arXiv
pre-print
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In this work, we proposed the utilization of approximate nearest neighbor search algorithms for the task of kNN classification in scRNA-seq data focusing on a particular methodology tailored for high dimensional ...
Regarding the classification process for scRNA-seq data, an appropriate method is the k Nearest Neighbor (kNN) classifier since it is usually utilized for large-scale prediction tasks due to its simplicity ...
APPROXIMATE K-NEAREST NEIGHBOR SEARCH k-Nearest Neighbor searching is still a very challenging task for high-dimensional data. ...
arXiv:2012.02149v1
fatcat:2ojbwhmrszg65e7wtof7uqjoru
Fast GPU-based locality sensitive hashing for k-nearest neighbor computation
2011
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '11
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We present an efficient GPU-based parallel LSH algorithm to perform approximate k-nearest neighbor computation in high-dimensional spaces. ...
The second level involves computing the Bi-Level LSH code for each item and constructing a hierarchical hash table. The hash table is based on parallel cuckoo hashing and Morton curves. ...
The problem of exact or approximate k-nearest neighbor search is well studied in the literature. ...
doi:10.1145/2093973.2094002
dblp:conf/gis/PanM11
fatcat:s4cm3nz7qjerrkhjdou7iipyr4
Forest hashing: Expediting large scale image retrieval
2013
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
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This paper introduces a hybrid method for searching large image datasets for approximate nearest neighbor items, specifically SIFT descriptors. ...
The basic idea behind our method is to create a serial system that first partitions approximate nearest neighbors using multiple kd-trees before calling upon locally designed spectral hashing tables for ...
It has been proven that approximate nearest neighbor (ANN) search works well for this task [7] , and since this requires no offline training it is practical for many applications with continually expanding ...
doi:10.1109/icassp.2013.6637938
dblp:conf/icassp/SpringerXLWK13
fatcat:pj3eixi27rcnjep7er2tr3hrbe
Fast Matching of Binary Features
2012
2012 Ninth Conference on Computer and Robot Vision
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For vector-based features, such as SIFT and SURF, the solution has been to use approximate nearest-neighbor search, but these existing algorithms are not suitable for binary features. ...
In this paper we introduce a new algorithm for approximate matching of binary features, based on priority search of multiple hierarchical clustering trees. ...
For matching binary features, the approximate nearest neighbor search algorithms used in the literature are mostly based on various hashing techniques such as locality sensitive hashing [2] , semantic ...
doi:10.1109/crv.2012.60
dblp:conf/crv/MujaL12
fatcat:66vkhphd2nbmbnsxqvck3wndru
Fast nearest neighbor search through sparse random projections and voting
2016
2016 IEEE International Conference on Big Data (Big Data)
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Efficient index structures for fast approximate nearest neighbor queries are required in many applications such as recommendation systems. ...
We demonstrate by extensive experiments on a wide variety of data sets that the method is faster than existing partitioning tree or hashing based approaches, making it the fastest available technique on ...
ACKNOWLEDGEMENTS This work was supported in part by the Finnish Funding Agency for Innovation (Project SPA), the Academy of Finland (Centre-of-Excellence COIN), and the DoCS graduate school of the University ...
doi:10.1109/bigdata.2016.7840682
dblp:conf/bigdataconf/HyvonenPTJTWCR16
fatcat:tfy5d26uh5bnnlxfcga7f2waoq
Compact projection: Simple and efficient near neighbor search with practical memory requirements
2010
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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We present a very simple algorithm for generating compact binary representations of imagery data, based on random projections. ...
Efficient similarity search across large image databases depends critically on the availability of compact image representations and good data structures for indexing them. ...
Locality Sensitive Hashing (LSH) builds on this property to give a stateof-the-art approximate nearest neighbor scheme [2] . ...
doi:10.1109/cvpr.2010.5539973
dblp:conf/cvpr/MinYWWHM10
fatcat:4pnenpygrzavnlbvp33ukz7qd4
Compact hash codes and data structures for efficient mobile visual search
2015
2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
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Index Terms-Mobile visual search, nearest neighbor search, hashing, SIFT. ...
In this paper we present an efficient method for mobile visual search that exploits compact hash codes and data structures for visual features retrieval. ...
Acknowledgments The authors thank Hervé Jégou for his help in running the code of his method. ...
doi:10.1109/icmew.2015.7169856
dblp:conf/icmcs/ErcoliBB15
fatcat:ipbm2tq7vvhmvokkuj5qofs73e
EFFICIENT IMAGE RETRIEVAL IN BIG DATABASE USING MULTI FEATURE DESCRIPTOR
2016
International Journal of Research in Engineering and Technology
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Multiview alignment hashing approach based on regularized kernel nonnegative matrix factorization(NMF), which can find a compact representation uncovering the hidden semantics and simultaneously respecting ...
Calculate hash for the image and this hash can be map for different images also. The existing method for image retrieval is Hashing technology. ...
Supports near(est) neighbor search (similar as before),
Works for points and rectangles, Avoids empty spaces,
Many variants: X-tree, SS-tree, SR-tree etc, Works well for
low dimensions. ...
doi:10.15623/ijret.2016.0516020
fatcat:wndeunahcrghjnq5inlw2tvigq
HDIdx: High-dimensional indexing for efficient approximate nearest neighbor search
2017
Neurocomputing
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Fast Nearest Neighbor (NN) search is a fundamental challenge in large-scale data processing and analytics, particularly for analyzing multimedia contents which are often of high dimensionality. ...
In this work, we present "HDIdx", an efficient high-dimensional indexing library for fast approximate NN search, which is open-source and written in Python. ...
Introduction Nearest neighbor (NN) search, also known as proximity search or similarity search, aims to find closest or most similar data points/items from a collection of data points/items. ...
doi:10.1016/j.neucom.2015.11.104
fatcat:h5gsn6m2yfbphfb4mxiapceoba
Trinary-Projection Trees for Approximate Nearest Neighbor Search
2014
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We address the problem of approximate nearest neighbor (ANN) search for visual descriptor indexing. ...
In addition, we provide an extension using multiple randomized trees for improved performance. We justify our approach on large scale local patch indexing and similar image search. ...
CONCLUSION In this paper, we present a novel hierarchical spatial partition tree for approximate nearest neighbor search. ...
doi:10.1109/tpami.2013.125
pmid:24356357
fatcat:dawzydol5na7be55fmcd3tfuse
Scalable Nearest Neighbor Algorithms for High Dimensional Data
2014
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Index Terms-Nearest neighbor search, big data, approximate search, algorithm configuration Ç M. Muja is with BitLit ...
for nearest neighbor matching. ...
Hashing Based Nearest Neighbor Techniques Perhaps the best known hashing based nearest neighbor technique is locality sensitive hashing (LSH) [29] , which uses a large number of hash functions with the ...
doi:10.1109/tpami.2014.2321376
pmid:26353063
fatcat:n42wwark2fdephku4jsjwfv5ky
Thick boundaries in binary space and their influence on nearest-neighbor search
2012
Pattern Recognition Letters
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Unfortunately, even if the similarity between vectors can be computed fast, exhaustive linear search remains impractical for truly large databases and Approximate Nearest Neighbor (ANN) search is still ...
A different approach to speeding up nearest-neighbor search is to binarize the real-valued descriptors using techniques such as Boosting [11], hashing [10, 12], PCA or LDA-based methods [13, 14], quantization ...
Approximate Nearest Neighbor Search Even though Nearest Neighbor search has been widely discussed in the literature, no known generic algorithm is both exact and more efficient than brute force search. ...
doi:10.1016/j.patrec.2012.08.006
fatcat:3znq765zt5gl7omss2khpy4zbm
Error-correcting output hashing in fast similarity search
2010
Proceedings of the Second International Conference on Internet Multimedia Computing and Service - ICIMCS '10
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Recently, hashing-based methods, which create compact and efficient codes that preserve data distribution, have received considerable attention due to their promising theoretical and empirical results. ...
An ideal hashing method 1) can naturally have out-of-sample extension; 2) has very low computational complexity; and 3) has significant improvement over linear search in the original space in terms of ...
For example, tree based approximate nearest neighbor methods were proposed to claim sublinear o(N ) time or logarithmatic O(log(N )) query time [7] . ...
doi:10.1145/1937728.1937730
dblp:conf/icimcs/YuCH10
fatcat:nz5t5nfn7fb3legywy5gefjcp4
Efficient approximate nearest neighbor search with integrated binary codes
2011
Proceedings of the 19th ACM international conference on Multimedia - MM '11
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The difficulty of exact nearest neighbor search has led to approximate solutions that sacrifice precision for efficiency. ...
Therefore, to improve nearest neighbor search efficiency, we proposed a novel binary code method called Integrated Binary Code (IBC) to get a compact set of similar neighbors. ...
For these binary code methods, searching nearest neighbors in Euclidean space is approximated by searching similar neighbors in terms of Hamming distances between codes. ...
doi:10.1145/2072298.2071971
dblp:conf/mm/ZhangGZL11
fatcat:ulalbkj5rfd5hmcitf3g2wtfny
Composite Quantization for Approximate Nearest Neighbor Search
2014
International Conference on Machine Learning
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This paper presents a novel compact coding approach, composite quantization, for approximate nearest neighbor search. ...
the distance only using the distance of the query to each selected element is enough for nearest neighbor search. ...
Conclusion In this paper, we present a compact coding approach, composite quantization, to approximate nearest neighbor search. ...
dblp:conf/icml/ZhangDW14
fatcat:om2vrxtcdvb5blitlmurkolywu
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