The new parallel multiclass stochastic gradient descent algorithms aim at classifying million images with very-high-dimensional signatures into thousands of classes. We extend the stochastic gradient descent (SGD) for support vector machines (SVM-SGD) in several ways to develop the new multiclass SVM-SGD for efficiently classifying large image datasets into many classes. We propose (1) a balanced training algorithm for learning binary SVM-SGD classifiers, and (2) a parallel training process of classifiers with several multi-core computers/grid. The evaluation on 1000 classes of ImageNet, ILSVRC 2010 shows that our algorithm is 270 times faster than the state-of-the-art linear classifier LIBLINEAR.
CITATION STYLE
Do, T.-N. (2014). Parallel multiclass stochastic gradient descent algorithms for classifying million images with very-high-dimensional signatures into thousands classes. Vietnam Journal of Computer Science, 1(2), 107–115. https://doi.org/10.1007/s40595-013-0013-2
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