PDF] Locality-sensitive binary codes from shift-invariant kernels
Por um escritor misterioso
Last updated 03 abril 2025
![PDF] Locality-sensitive binary codes from shift-invariant kernels](https://d3i71xaburhd42.cloudfront.net/1b20afbd2d2a349737ed3dc246e44bbdba203190/8-Figure4-1.png)
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.
![PDF] Locality-sensitive binary codes from shift-invariant kernels](https://journals.sagepub.com/cms/10.1177/09670335221078356/asset/images/large/10.1177_09670335221078356-fig10.jpeg)
Hyperspectral imaging for classification of bulk grain samples with deep convolutional neural networks - Erik Schou Dreier, Klavs Martin Sorensen, Toke Lund-Hansen, Birthe Møller Jespersen, Kim Steenstrup Pedersen, 2022
![PDF] Locality-sensitive binary codes from shift-invariant kernels](https://i1.rgstatic.net/publication/339477698_Unsupervised_Ensemble_Hashing_Boosting_Minimum_Hamming_Distance/links/5e5512d1299bf1bdb8396adf/largepreview.png)
PDF) Unsupervised Ensemble Hashing: Boosting Minimum Hamming Distance
![PDF] Locality-sensitive binary codes from shift-invariant kernels](https://dl.acm.org/cms/asset/22371fdd-3f18-428c-83db-25b58cceb8af/tois-2022-0329-f12.jpg)
Invariant Node Representation Learning under Distribution Shifts with Multiple Latent Environments
![PDF] Locality-sensitive binary codes from shift-invariant kernels](https://iiif.elifesciences.org/lax:78606%2Felife-78606-fig7-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://ietresearch.onlinelibrary.wiley.com/cms/asset/fe4c965b-7ddb-41e8-a2f1-043bb8bb79ed/cvi2bf00303-fig-0004-m.jpg)
Kernel locality‐constrained sparse coding for head pose estimation - Kim - 2016 - IET Computer Vision - Wiley Online Library
![PDF] Locality-sensitive binary codes from shift-invariant kernels](https://iiif.elifesciences.org/lax:78606%2Felife-78606-fig2-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://www.science.org/cms/10.1126/sciadv.abl4995/asset/36c4fd04-f83e-4bb0-96b6-125216a375c7/assets/images/large/sciadv.abl4995-f1.jpg)
A spatiotemporal mechanism of visual attention: Superdiffusive motion and theta oscillations of neural population activity patterns
![PDF] Locality-sensitive binary codes from shift-invariant kernels](https://d3i71xaburhd42.cloudfront.net/b3f09ea2a8cc1d82c2b27d71dd8f7451d178beaf/5-Figure4-1.png)
PDF] Iterative quantization: A procrustean approach to learning binary codes
![PDF] Locality-sensitive binary codes from shift-invariant kernels](https://nicholas.carlini.com/writing/2019/advex_plot.png)
A Complete List of All Adversarial Example Papers
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