Authors:
Ryoma Hasegawa
and
Kazuhiro Hotta
Affiliation:
Meijo University, Japan
Keyword(s):
Deep Learning, Convolutional Neural Network, PCANet, Partial Least Squares Regression, PLSNet, Clustering.
Abstract:
In this paper, we propose an image classification method using Partial Least Squares regression (PLS) and
clustering. PLSNet is a simple network using PLS for image classification and obtained high accuracies on
the MNIST and CIFAR-10 datasets. It crops a lot of local regions from training images as explanatory
variables, and their class labels are used as objective variables. Then PLS is applied to those variables, and
some filters are obtained. However, there are a variety of local regions in each class, and intra-class variance
is large. Therefore, we consider that local regions in each class should be divided and handled separately. In
this paper, we apply clustering to local regions in each class and make a set from a cluster of all classes.
There are some sets whose number is the number of clusters. Then we apply PLSNet to each set. By doing
the processes, we obtain some feature vectors per image. Finally, we train SVM for each feature vector and
classify the images by voting t
he result of SVM. Our PLSNet obtained 82.42% accuracy on the CIFAR-10
dataset. This accuracy is 1.69% higher than PLSNet without clustering and an attractive result of the
methods without CNN.
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