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Accelerated Large Scale Optimization by Concomitant Hashing

  • Yadong Mu
  • John Wright
  • Shih-Fu Chang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)

Abstract

Traditional locality-sensitive hashing (LSH) techniques aim to tackle the curse of explosive data scale by guaranteeing that similar samples are projected onto proximal hash buckets. Despite the success of LSH on numerous vision tasks like image retrieval and object matching, however, its potential in large-scale optimization is only realized recently. In this paper we further advance this nascent area. We first identify two common operations known as the computational bottleneck of numerous optimization algorithms in a large-scale setting, i.e., min/max inner product. We propose a hashing scheme for accelerating min/max inner product, which exploits properties of order statistics of statistically correlated random vectors. Compared with other schemes, our algorithm exhibits improved recall at a lower computational cost. The effectiveness and efficiency of the proposed method are corroborated by theoretic analysis and several important applications. Especially, we use the proposed hashing scheme to perform approximate ℓ1 regularized least squares with dictionaries with millions of elements, a scale which is beyond the capability of currently known exact solvers. Nonetheless, it is highlighted that the focus of this paper is not on a new hashing scheme for approximate nearest neighbor problem. It exploits a new application for the hashing techniques and proposes a general framework for accelerating a large variety of optimization procedures in computer vision.

Keywords

Sparse Code Mean Average Precision Random Projection Orthogonal Match Pursuit Gaussian Process Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yadong Mu
    • 1
  • John Wright
    • 1
  • Shih-Fu Chang
    • 1
  1. 1.Electrical Engineering DepartmentColumbia UniversityNew YorkUSA

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