Latent-lSVM classification of very high-dimensional and large-scale multi-class datasets
Abstract
We propose a new parallel learning algorithm of latent local support vector machines
(SVM), called latent-lSVM for effectively classifying very high-dimensional and large-scale
multi-class datasets. The common framework of texts/images classification tasks using the
Bag-Of-(visual)-Words model for the data representation leads to hard classification problem
with thousands of dimensions and hundreds of classes.Ourlatent-lSVM algorithm performs these
complex tasks into two main steps. The first one is to use latent Dirichlet allocation for assigning
the datapoint (text/image) to some topics (clusters) with the corresponding probabilities. This
aims at reducing the number of classes and the number of datapoints in the cluster compared
to the full dataset, followed by the second one: to learn in a parallel way nonlinear SVM models
to classify data clusters locally. The numerical test results on nine real datasets show that the
latent-lSVM algorithm achieves very high accuracy compared to state-of-the-art algorithms. An
example of its effectiveness is given with an accuracy of 70.14% obtained in the classification of
Book dataset having 100 000 individuals in 89 821 dimensional input space and 661 classes in
11.2minutes using a PC Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores.