Coding for Random Projections

Abstract

The method of random projections has become very popular for large-scale applications in statistical learning, information retrieval, bio-informatics and other applications. Using a well-designed coding scheme for the projected data, which determines the number of bits needed for each projected value and how to allocate these bits, can significantly improve the effectiveness of the algorithm, in storage cost as well as computational speed. In this paper, we study a number of simple coding schemes, focusing on the task of similarity estimation and on an application to training linear classifiers. We demonstrate that uniform quantization outperforms the standard existing influential method (Datar et. al. 2004). Indeed, we argue that in many cases coding with just a small number of bits suffices. Furthermore, we also develop a non-uniform 2-bit coding scheme that generally performs well in practice, as confirmed by our experiments on training linear support vector machines (SVM).

References in corpus (3)

• Experiments with Random Projection
• Coding for Random Projections and Approximate Near Neighbor Search
• Sign Stable Projections, Sign Cauchy Projections and Chi-Square Kernels

Cited by in corpus (7)

• Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS)
• Sub-Linear Privacy-Preserving Near-Neighbor Search
• Binary and Multi-Bit Coding for Stable Random Projections
• Asymmetric Minwise Hashing
• Quantization Algorithms for Random Fourier Features
• Sign Stable Random Projections for Large-Scale Learning
• 2-Bit Random Projections, NonLinear Estimators, and Approximate Near Neighbor Search