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Locality sensitive hashing

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https://www.readbyqxmd.com/read/29018478/ultrafast-comparison-of-personal-genomes-via-precomputed-genome-fingerprints
#1
Gustavo Glusman, Denise E Mauldin, Leroy E Hood, Max Robinson
We present an ultrafast method for comparing personal genomes. We transform the standard genome representation (lists of variants relative to a reference) into "genome fingerprints" via locality sensitive hashing. The resulting genome fingerprints can be meaningfully compared even when the input data were obtained using different sequencing technologies, processed using different pipelines, represented in different data formats and relative to different reference versions. Furthermore, genome fingerprints are robust to up to 30% missing data...
2017: Frontiers in Genetics
https://www.readbyqxmd.com/read/28981422/phenotype-prediction-from-metagenomic-data-using-clustering-and-assembly-with-multiple-instance-learning-camil
#2
Mohammad Arifur Rahman, Nathan LaPierre, Huzefa Rangwala
The recent advent of Metagenome Wide Association Studies (MGWAS) provides insight into the role of microbes on human health and disease. However, the studies present several computational challenges. In this paper we demonstrate a novel, efficient, and effective Multiple Instance Learning (MIL) based computational pipeline to predict patient phenotype from metagenomic data. MIL methods have the advantage that besides predicting the clinical phenotype, we can infer the instance level label or role of microbial sequence reads in the specific disease...
October 4, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28953425/shared-nearest-neighbor-clustering-in-a-locality-sensitive-hashing-framework
#3
Sawsan Kanj, Thomas Brüls, Stéphane Gazut
We present a new algorithm to cluster high-dimensional sequence data and its application to the field of metagenomics, which aims at reconstructing individual genomes from a mixture of genomes sampled from an environmental site, without any prior knowledge of reference data (genomes) or the shape of clusters. Such problems typically cannot be solved directly with classical approaches seeking to estimate the density of clusters, for example, using the shared nearest neighbors (SNN) rule, due to the prohibitive size of contemporary sequence datasets...
September 27, 2017: Journal of Computational Biology: a Journal of Computational Molecular Cell Biology
https://www.readbyqxmd.com/read/28771497/sinc-saliency-injected-neural-codes-for-representation-and-efficient-retrieval-of-medical-radiographs
#4
Jamil Ahmad, Muhammad Sajjad, Irfan Mehmood, Sung Wook Baik
Medical image collections contain a wealth of information which can assist radiologists and medical experts in diagnosis and disease detection for making well-informed decisions. However, this objective can only be realized if efficient access is provided to semantically relevant cases from the ever-growing medical image repositories. In this paper, we present an efficient method for representing medical images by incorporating visual saliency and deep features obtained from a fine-tuned convolutional neural network (CNN) pre-trained on natural images...
2017: PloS One
https://www.readbyqxmd.com/read/28508884/a-hybrid-cloud-read-aligner-based-on-minhash-and-kmer-voting-that-preserves-privacy
#5
Victoria Popic, Serafim Batzoglou
Low-cost clouds can alleviate the compute and storage burden of the genome sequencing data explosion. However, moving personal genome data analysis to the cloud can raise serious privacy concerns. Here, we devise a method named Balaur, a privacy preserving read mapper for hybrid clouds based on locality sensitive hashing and kmer voting. Balaur can securely outsource a substantial fraction of the computation to the public cloud, while being highly competitive in accuracy and speed with non-private state-of-the-art read aligners on short read data...
May 16, 2017: Nature Communications
https://www.readbyqxmd.com/read/28268827/stratified-locality-sensitive-hashing-for-accelerated-physiological-time-series-retrieval
#6
Yongwook Bryce Kim, Erik Hemberg, Una-May O'Reilly
We introduce stratified locality-sensitive hashing (SLSH) for retrieving similar physiological waveform time series. SLSH further accelerates the sublinear retrieval time obtained by the standard locality-sensitive hashing (LSH) method. The standard family of locality-sensitive hash functions is limited to provide only a single perspective on the data due to its one-to-one relationship to a distinct distance function for measuring similarity. SLSH incorporates multiple locality-sensitive hash families with various distance functions enabling it to examine the data with more diverse and refined perspectives...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268443/analysis-of-locality-sensitive-hashing-for-fast-critical-event-prediction-on-physiological-time-series
#7
Yongwook Bryce Kim, Una-May O'Reilly
We apply the sublinear time, scalable locality-sensitive hashing (LSH) and majority discrimination to the problem of predicting critical events based on physiological waveform time series. Compared to using the linear exhaustive k-nearest neighbor search, our proposed method vastly speeds up prediction time up to 25 times while sacrificing only 1% of accuracy when demonstrated on an arterial blood pressure dataset extracted from the MIMIC2 database. We compare two widely used variants of LSH, the bit sampling based (L1LSH) and the random projection based (E2LSH) methods to measure their direct impact on retrieval and prediction accuracy...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227023/stratified-locality-sensitive-hashing-for-accelerated-physiological-time-series-retrieval
#8
Yongwook Bryce Kim, Erik Hemberg, Una-May O'Reilly, Yongwook Bryce Kim, Erik Hemberg, Una-May O'Reilly, Yongwook Bryce Kim, Una-May O'Reilly, Erik Hemberg
We introduce stratified locality-sensitive hashing (SLSH) for retrieving similar physiological waveform time series. SLSH further accelerates the sublinear retrieval time obtained by the standard locality-sensitive hashing (LSH) method. The standard family of locality-sensitive hash functions is limited to provide only a single perspective on the data due to its one-to-one relationship to a distinct distance function for measuring similarity. SLSH incorporates multiple locality-sensitive hash families with various distance functions enabling it to examine the data with more diverse and refined perspectives...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226614/analysis-of-locality-sensitive-hashing-for-fast-critical-event-prediction-on-physiological-time-series
#9
Yongwook Bryce Kim, Una-May O'Reilly, Yongwook Bryce Kim, Una-May O'Reilly, Yongwook Bryce Kim, Una-May O'Reilly
We apply the sublinear time, scalable locality-sensitive hashing (LSH) and majority discrimination to the problem of predicting critical events based on physiological waveform time series. Compared to using the linear exhaustive k-nearest neighbor search, our proposed method vastly speeds up prediction time up to 25 times while sacrificing only 1% of accuracy when demonstrated on an arterial blood pressure dataset extracted from the MIMIC2 database. We compare two widely used variants of LSH, the bit sampling based (L1LSH) and the random projection based (E2LSH) methods to measure their direct impact on retrieval and prediction accuracy...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28127592/low-density-locality-sensitive-hashing-boosts-metagenomic-binning
#10
Yunan Luo, Jianyang Zeng, Bonnie Berger, Jian Peng
No abstract text is available yet for this article.
April 2016: Research in Computational Molecular Biology: ... Annual International Conference, RECOMB ...: Proceedings
https://www.readbyqxmd.com/read/28114084/binary-set-embedding-for-cross-modal-retrieval
#11
Mengyang Yu, Li Liu, Ling Shao
Cross-modal retrieval is such a challenging topic that traditional global representations would fail to bridge the semantic gap between images and texts to a satisfactory level. Using local features from images and words from documents directly can be more robust for the scenario with large intraclass variations and small interclass discrepancies. In this paper, we propose a novel unsupervised binary coding algorithm called binary set embedding (BSE) to obtain meaningful hash codes for local features from the image domain and words from text domain...
September 27, 2016: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28113786/in-defense-of-locality-sensitive-hashing
#12
Kun Ding, Chunlei Huo, Bin Fan, Shiming Xiang, Chunhong Pan
Hashing-based semantic similarity search is becoming increasingly important for building large-scale content-based retrieval system. The state-of-the-art supervised hashing techniques use flexible two-step strategy to learn hash functions. The first step learns binary codes for training data by solving binary optimization problems with millions of variables, thus usually requiring intensive computations. Despite simplicity and efficiency, locality-sensitive hashing (LSH) has never been recognized as a good way to generate such codes due to its poor performance in traditional approximate neighbor search...
October 24, 2016: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/27933251/robust-image-hashing-using-ring-partition-pgnmf-and-local-features
#13
Ram Kumar Karsh, R H Laskar, Bhanu Bhai Richhariya
BACKGROUND: Image authentication is one of the challenging research areas in the multimedia technology due to the availability of image editing tools. Image hash may be used for image authentication which should be invariant to perceptually similar image and sensitive to content changes. The challenging issue in image hashing is to design a system which simultaneously provides rotation robustness, desirable discrimination, sensitivity and localization of forged area with minimum hash length...
2016: SpringerPlus
https://www.readbyqxmd.com/read/27831879/a-reliable-order-statistics-based-approximate-nearest-neighbor-search-algorithm
#14
Luisa Verdoliva, Davide Cozzolino, Giovanni Poggi
We propose a new algorithm for fast approximate nearest neighbor search based on the properties of ordered vectors. Data vectors are classified based on the index and sign of their largest components, thereby partitioning the space in a number of cones centered in the origin. The query is itself classified, and the search starts from the selected cone and proceeds to neighboring ones. Overall, the proposed algorithm corresponds to locality sensitive hashing in the space of directions, with hashing based on the order of components...
January 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27662689/breast-histopathological-image-retrieval-based-on-latent-dirichlet-allocation
#15
Yibing Ma, Zhiguo Jiang, Haopeng Zhang, Fengying Xie, Yushan Zheng, Huaqiang Shi, Yu Zhao
In the field of pathology, whole slide image (WSI) has become the major carrier of visual and diagnostic information. Content based image retrieval among WSIs can aid the diagnosis of an unknown pathological image by finding its similar regions in WSIs with diagnostic information. However, the huge size and complex content of WSI pose several challenges for retrieval. In this paper, we propose an unsupervised, accurate and fast retrieval method for breast histopathological image. Specifically, the method presents local statistical feature of nuclei for morphology and distribution of nuclei, and employs Gabor feature to describe texture information...
September 20, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/26737617/large-scale-physiological-waveform-retrieval-via-locality-sensitive-hashing
#16
Yongwook Bryce Kim, Una-May O'Reilly
We propose a fast, scalable locality-sensitive hashing method for the problem of retrieving similar physiological waveform time series. When compared to the naive k-nearest neighbor search, the method vastly speeds up the retrieval time of similar physiological waveforms without sacrificing significant accuracy. Our result shows that we can achieve 95% retrieval accuracy or better with up to an order of magnitude of speed-up. The extra time required in advance to create the optimal data structure is recovered when query quantity equals 15% of the repository, while the method incurs a trivial additional memory cost...
August 2015: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/26571531/fast-localization-in-large-scale-environments-using-supervised-indexing-of-binary-features
#17
Youji Feng, Lixin Fan, Yihong Wu
The essence of image-based localization lies in matching 2D key points in the query image and 3D points in the database. State-of-the-art methods mostly employ sophisticated key point detectors and feature descriptors, e.g., Difference of Gaussian (DoG) and Scale Invariant Feature Transform (SIFT), to ensure robust matching. While a high registration rate is attained, the registration speed is impeded by the expensive key point detection and the descriptor extraction. In this paper, we propose to use efficient key point detectors along with binary feature descriptors, since the extraction of such binary features is extremely fast...
January 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/26448093/corrigendum-assembling-large-genomes-with-single-molecule-sequencing-and-locality-sensitive-hashing
#18
Konstantin Berlin, Sergey Koren, Chen-Shan Chin, James P Drake, Jane M Landolin, Adam M Phillippy
No abstract text is available yet for this article.
October 2015: Nature Biotechnology
https://www.readbyqxmd.com/read/26441458/structure-sensitive-hashing-with-adaptive-product-quantization
#19
Xianglong Liu, Bowen Du, Cheng Deng, Ming Liu, Bo Lang
Hashing has been proved as an attractive solution to approximate nearest neighbor search, owing to its theoretical guarantee and computational efficiency. Though most of prior hashing algorithms can achieve low memory and computation consumption by pursuing compact hash codes, however, they are still far beyond the capability of learning discriminative hash functions from the data with complex inherent structure among them. To address this issue, in this paper, we propose a structure sensitive hashing based on cluster prototypes, which explicitly exploits both global and local structures...
October 2016: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/26372204/coherency-sensitive-hashing
#20
Simon Korman, Shai Avidan
Coherency Sensitive Hashing (CSH) extends Locality Sensitivity Hashing (LSH) and PatchMatch to quickly find matching patches between two images. LSH relies on hashing, which maps similar patches to the same bin, in order to find matching patches. PatchMatch, on the other hand, relies on the observation that images are coherent, to propagate good matches to their neighbors in the image plane, using random patch assignment to seed the initial matching. CSH relies on hashing to seed the initial patch matching and on image coherence to propagate good matches...
June 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
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