Practical data-dependent metric compression with provable guarantees
Author(s)Indyk, Piotr; Razenshteyn, Ilya; Wagner, Tal
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We introduce a new distance-preserving compact representation of multidimensional point-sets. Given n points in a d-dimensional space where each coordinate is represented using B bits (i.e., dB bits per point), it produces a representation of size O(dlog(dB/e) + logn) bits per point from which one can approximate the distances up to a factor of 1 ± e. Our algorithm almost matches the recent bound of  while being much simpler. We compare our algorithm to Product Quantization (PQ) , a state of the art heuristic metric compression method. We evaluate both algorithms on several data sets: SIFT (used in ), MNIST , New York City taxi time series  and a synthetic one-dimensional data set embedded in a high-dimensional space. With appropriately tuned parameters, our algorithm produces representations that are comparable to or better than those produced by PQ, while having provable guarantees on its performance.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
31st Conference on Neural Information Processing Systems (NIPS 2017)
Neural Information Processing Systems Foundation, Inc.
Indyk, Piotr, Ilya Razenshteyn, and Tal Wagner. "Practical Data-Dependent Metric Compression with Provable Guarantees." 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, California, USA, 4-9 December, 2017, NIPS, 2017. © Neural Information Processing Systems Foundation, Inc.
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