2009
NIPS
NeurIPS 2009
Locality-sensitive binary codes from shift-invariant kernels
Abstract
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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— Algorithms
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— shift-invariant kernel
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— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
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Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Core Methods > Metric Learning
Machine Learning > Optimization & Theory > Optimization
Computer Science > Foundations > Algorithms
Machine Learning > Core Methods > Dimensionality Reduction
Data Science & Analytics > Applications > Information Retrieval
Machine Learning > Learning Types > Metric Learning