In this paper, we present a study on sample preselection in large training data set for CNN-based classification. To do so, we structure the input data set in a network representation, namely the Relative Neighbourhood Graph, and then extract some vectors of interest. The proposed preselection method is evaluated in the context of Handwritten Character Recognition, by using two data sets, up to several hundred thousands of images. It is shown that the graph-based preselection can reduce the training data set without degrading the recognition accuracy of a non pre-trained CNN shallow model.
In this study, we have chosen a graph-based condensing sample preselection strategy. Indeed, an extensive series of work have been performed on the relevance of proximity graphs @cite_16 to preselect samples in classification training set. More specifically, we have selected the RNG that has been recently proven a good fit to preselect high-dimensional samples @cite_2 in large training data sets @cite_9 . [dash pattern=on 5pt off 5pt, pattern color=black] (10.0,-3.0) arc (-90:90:3cm); [dash pattern=on 5pt off 5pt, pattern color=black] (13.0,3.0) arc (90:270:3cm); [color=black] (10.0,0.0) circle (3.5pt); [color=black] (9.50,0.0) node @math ; [color=black] (13.0,0.0) circle (3.5pt); [color=black] (13.50,0.0) node @math ; [color=black] (14.0,2.0) circle (3.5pt); [color=black] (14.25,2.3) node @math ;
@cite_9 @cite_16 @cite_2
2151245131 113115064 100451342
We propose a pre-selection method for training support vector machines (SVM) with a large-scale dataset. Specifically, the proposed method selects patterns around the class boundary and the selected data is fed to train an SVM. For the selection, that is, searching for boundary patterns, we utilize a relative neighborhood graph (RNG). An RNG has an edge for each pair of neighboring patterns and thus, we can find boundary patterns by looking for edges connecting patterns from different classes. Through large-scale handwritten digit pattern recognition experiments, we show that the proposed pre-selection method accelerates SVM training process 5–15 times faster without degrading recognition accuracy. Previous experiments with low dimensional data sets have shown that Gabriel graph methods for instance-based learning are among the best machine learning algorithms for pattern classification applications. However, as the dimensionality of the data grows large, all data points in the training set tend to become Gabriel neighbors of each other, bringing the efficacy of this method into question. Indeed, it has been conjectured that for high-dimensional data, proximity graph methods that use sparser graphs, such as relative neighbor graphs (RNG) and minimum spanning trees (MST) would have to be employed in order to maintain their privileged status. Here the performance of proximity graph methods, in instance-based learning, that employ Gabriel graphs, relative neighborhood graphs, and minimum spanning trees, are compared experimentally on high-dimensional data sets. These methods are also compared empirically against the traditional k-NN rule and support vector machines (SVMs), the leading competitors of proximity graph methods.