PDF] Locality-sensitive binary codes from shift-invariant kernels
Por um escritor misterioso
Last updated 04 abril 2025
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This paper introduces a simple distribution-free encoding scheme based on random projections, such that the expected Hamming distance between the binary codes of two vectors is related to the value of a shift-invariant kernel between the vectors. This paper addresses the problem of designing binary codes for high-dimensional data such that vectors that are similar in the original space map to similar binary strings. We introduce a simple distribution-free encoding scheme based on random projections, such that the expected Hamming distance between the binary codes of two vectors is related to the value of a shift-invariant kernel (e.g., a Gaussian kernel) between the vectors. We present a full theoretical analysis of the convergence properties of the proposed scheme, and report favorable experimental performance as compared to a recent state-of-the-art method, spectral hashing.
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Group sparse based locality – sensitive dictionary learning for video semantic analysis
![PDF] Locality-sensitive binary codes from shift-invariant kernels](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41467-022-29268-7/MediaObjects/41467_2022_29268_Fig1_HTML.png)
Current progress and open challenges for applying deep learning across the biosciences
![PDF] Locality-sensitive binary codes from shift-invariant kernels](https://iiif.elifesciences.org/lax:78606%2Felife-78606-fig1-v2.tif/full/1500,/0/default.jpg)
Population codes enable learning from few examples by shaping inductive bias
![PDF] Locality-sensitive binary codes from shift-invariant kernels](https://pub.mdpi-res.com/remotesensing/remotesensing-14-06358/article_deploy/html/images/remotesensing-14-06358-ag.png?1671680125)
Remote Sensing, Free Full-Text
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Machine learning methods for sign language recognition: A critical review and analysis - ScienceDirect
![PDF] Locality-sensitive binary codes from shift-invariant kernels](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41598-022-18402-6/MediaObjects/41598_2022_18402_Fig1_HTML.png)
Convolution neural network with batch normalization and inception-residual modules for Android malware classification
![PDF] Locality-sensitive binary codes from shift-invariant kernels](https://d3i71xaburhd42.cloudfront.net/1b20afbd2d2a349737ed3dc246e44bbdba203190/7-Figure3-1.png)
PDF] Locality-sensitive binary codes from shift-invariant kernels
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Evaluation of noise robustness for local binary pattern descriptors in texture classification – topic of research paper in Medical engineering. Download scholarly article PDF and read for free on CyberLeninka open science
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References - Sampling Theory
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Neighbourhood Preserving Quantisation for LSH SIGIR Poster
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