Bolt: Accelerated Data Mining with Fast Vector Compression

Vectors of data are at the heart of machine learning and data mining. Recently, vector quantization methods have shown great promise in reducing both the time and space costs of operating on vectors. We introduce a vector quantization algorithm that can compress vectors over 12x faster than existing techniques while also accelerating approximate vector operations such as distance and dot product computations by up to 10x. Because it can encode over 2GB of vectors per second, it makes vector quantization cheap enough to employ in many more circumstances. For example, using our technique to compute approximate dot products in a nested loop can multiply matrices faster than a state-of-the-art BLAS implementation, even when our algorithm must first compress the matrices. In addition to showing the above speedups, we demonstrate that our approach can accelerate nearest neighbor search and maximum inner product search by over 100x compared to floating point operations and up to 10x compared to other vector quantization methods. Our approximate Euclidean distance and dot product computations are not only faster than those of related algorithms with slower encodings, but also faster than Hamming distance computations, which have direct hardware support on the tested platforms. We also assess the errors of our algorithm's approximate distances and dot products, and find that it is competitive with existing, slower vector quantization algorithms.Comment: Research track paper at KDD 201

Efficient storage and decoding of SURF feature points

Practical use of SURF feature points in large-scale indexing and retrieval engines requires an efficient means for storing and decoding these features. This paper investigates several methods for compression and storage of SURF feature points, considering both storage consumption and disk-read efficiency. We compare each scheme with a baseline plain-text encoding scheme as used by many existing SURF implementations. Our final proposed scheme significantly reduces both the time required to load and decode feature points, and the space required to store them on disk