Coding for Random Projections
This work addresses storage and computational efficiency for similarity estimation and linear classifier training in fields like statistical learning and bioinformatics, representing an incremental improvement over existing methods.
The paper tackles the problem of improving random projections for large-scale applications by designing efficient coding schemes for the projected data, demonstrating that uniform quantization outperforms the standard method and that a non-uniform 2-bit scheme performs well in training linear SVMs.
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).