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Showing 1 to 16 of 16 for “"Locality-Sensitive Hashing"”.
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Beyond locality-sensitive hashing
… and the first data structure that bypasses a locality-sensitive hashing lower bound proved by O'Donnell, Wu and Zhou (ICS 2011). By known reductions we obtain a data structure for the Hamming space and ... which is the first improvement over the result of Indyk and Motwani (STOC 1998).
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Accelerated clustering through locality-sensitive hashing
We obtain improved running times for two algorithms for clustering data: the expectation-maximization (EM) algorithm and Lloyd's algorithm. The EM algorithm is a heuristic for finding a mixture of k normal distributions in Rd that maximizes the probability of drawing n given data points. Lloyd's …
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Physiological time series retrieval and prediction with locality-sensitive hashing
… order to achieve this goal, we propose to apply locality-sensitive hashing (LSH), which supports a very fast approximate nearest neighbor search in high dimensions. We empirically demonstrate that LSH based retrieval and prediction methods vastly speed up querying time while sacrificing only a …
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Resource-Efficient Machine Learning via Count-Sketches and Locality-Sensitive Hashing (LSH)
… items. In this thesis, we explore how to use locality-sensitive hashing (LSH) and the count-sketch data structure for addressing the computational and memory challenges in four distinct areas. (1) The LSH Sampling algorithm uses the LSH data structure as an adaptive sampler. We demonstrate …
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Evaluating parameter optimization in locality-sensitive hashing for high-dimensional physiological waveforms
… architecture to inform parameter choice for locality-sensitive hashing methods used towards identifying similarity in physiological waveform time-series data. The goal is to achieve increased probability of successful patient outcomes in emergency rooms by tackling the problem of efficient …
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An analysis of SIFT object recognition with an emphasis on landmark detection
… built a system using SIFT object recognition and Locality-Sensitive Hashing to quickly and accurately detect landmarks with accuracies ranging from 85-95%. I have also compared PCA-SIFT, a newly developed feature descriptor, to SIFT, and have found that SIFT outperforms it only particular data …
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High-dimensional similarity search and sketching : algorithms and hardness
… that, for the first time, improves upon the Locality-Sensitive Hashing (LSH) framework. The key new insight is to use random space partitions that depend on the dataset. ' An implementation of the core component of the above algorithm, which is released as FALCONN: a new C++ library for …
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From bits to information : learning meets compressive sensing
… lossy image compression and retrieval via locality sensitive hashing, locally linear estimation in large scale learning and Fourier sampling for phase retrieval - of particular interest in X-ray crystallography and super-resolution diffraction imaging applications. Our analysis of …
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Optimizing Data Compression via Data Reordering Strategies
… using an advanced data encoding technique called locality-sensitive hashing (LSH). One approach utilizes clustering for data reordering, while the other employs a heuristic-based solver for the Travelling Salesman Problem (TSP). The data encoding process enables the identification of internal …
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Secure Discovery of Genetic Relatives across Large-Scale and Distributed Genomic Datasets
… maintaining accurate detection through a novel locality-sensitive hashing approach. We assign individuals who are likely to be related together into buckets and then test relationships only between individuals in matching buckets across parties. To this end, we construct an effective hash …
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Community Classification of the Protein Universe
… with existing protein families in Pfam, using locality sensitive hashing. In the third chapter, I discuss the use of literature search in protein family database curation, and the existing literature resources used by protein family database curators. I then develop a system for performing …
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Large Scale Nearest Neighbor Search - Theories, Algorithms, and Applications
… structures, for example, tree based partition, hashing based partition, clustering/quantization based partition, etc. From the viewpoint of how the data partition function is generated, the partition methods can be grouped into two main categories: 1. data independent (random) partition such as …
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Search over Encrypted Data in Cloud Computing
… novel design that incorporates Bloom filter and Locality-Sensitive Hashing to fulfill the security and function requirements of the problem. Secondly, we propose a secure index which is based on the most popular index structure, i.e., the inverted index. Our innovative design provides privacy …
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Nearest neighbor search for cyro-electron microscopy images
… the dataset is large. Therefore, strategies like locality sensitive hashing (LSH) and some neural network methods are taken into consideration. The LSH method maps similar items into the same cell in a hash table, which effectively shortens the query time of searching for k-nearest neighbors, …
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Tackling the current limitations of bacterial taxonomy with genome-based classification and identification on a crowdsourcing Web service
Bacterial taxonomy is the science of classifying, naming, and identifying bacteria. The scope and practice of taxonomy has evolved through history with our understanding of life and our growing and changing needs in research, medicine, and industry. As in animal and plant taxonomy, the species is …
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Incremental learning for large-scale stream data and its application to cybersecurity
… Here, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) as a classifier model with data selection. In this data selection, the same or similar spam emails that have already been learned are quickly searched for a hash table using Locally Sensitive Hashing, and such …