An Alternating Optimization Scheme for Binary Sketches
Year: 2025
Type: article
Abstract: Searching for similar objects in intrinsically high-dimensional data sets is a challenging task. The use of compact sketches has been proposed for faster similarity search using linear scans. Binary sketches are one such approach to find a good mapping from the original data space to bit strings of a fixed length. These bit strings can be compared efficiently using only few XOR and bit count operations, replacing costly similarity computations with an inexpensive approximation. We propose a new scheme to initialize and improve binary sketches for similarity search in Euclidean spaces. Our optimization iteratively improves the quality of the sketches with a form of orthogonalization. We provide empirical evidence that the quality of the sketches has a peak beyond which it is not correlated to neither bit independence nor bit balance, which contradicts a previous hypothesis in the literature. Regularization in the form of noise added to the training data can turn the peak into a plateau and applying the optimization in a stochastic fashion, i.e., training on smaller subsets of the data, allows for rapid initialization. We provide a loss function that allows to approximate the same objective using neural network frameworks such as PyTorch, elevating the approach to GPU-based training.
Source: Information Systems
Authors: Erik Thordsen, Erich Schubert
Institution: TU Dortmund University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,410
OpenAlex ID: W4410264259