PDF] Locality-sensitive binary codes from shift-invariant kernels

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.

Performance of two complementary machine-learned potentials in modelling chemically complex systems

PDF] Iterative quantization: A procrustean approach to learning binary codes

Locality-sensitive binary codes from shift-invariant kernels

Mathematics, Free Full-Text

Learning Binary Hash Codes for Large-Scale Image Search

Convolution neural network with batch normalization and inception-residual modules for Android malware classification

PDF] Locality-sensitive binary codes from shift-invariant kernels

PDF] Angular Quantization-based Binary Codes for Fast Similarity Search

Defect detection in atomic-resolution images via unsupervised learning with translational invariance

Population codes enable learning from few examples by shaping inductive bias

Sensors, Free Full-Text

AI, Free Full-Text