Memory-efficient large-scale linear support vector machine

Abdullah Alrajeh; Akiko Takeda; Mahesan Niranjan

doi:10.1117/12.2180925

14 February 2015 Memory-efficient large-scale linear support vector machine

Abdullah Alrajeh, Akiko Takeda, Mahesan Niranjan

Proceedings Volume 9445, Seventh International Conference on Machine Vision (ICMV 2014); 944527 (2015) https://doi.org/10.1117/12.2180925
Event: Seventh International Conference on Machine Vision (ICMV 2014), 2014, Milan, Italy

Abstract

Stochastic gradient descent has been advanced as a computationally efficient method for large-scale problems. In classification problems, many proposed linear support vector machines are very effective. However, they assume that the data is already in memory which might be not always the case. Recent work suggests a classical method that divides such a problem into smaller blocks then solves the sub-problems iteratively. We show that a simple modification of shrinking the dataset early will produce significant saving in computation and memory. We further find that on problems larger than previously considered, our approach is able to reach solutions on top-end desktop machines while competing methods cannot.

Citation Download Citation

Abdullah Alrajeh, Akiko Takeda, and Mahesan Niranjan "Memory-efficient large-scale linear support vector machine", Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 944527 (14 February 2015); https://doi.org/10.1117/12.2180925

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