MPPCANet: A feedforward learning strategy for few-shot image classification

The main learning strategy of the PCANet is using Principal Component Analysis (PCA) for learning the convolutional filters from the data. The assumption that all the image patches are sampled from a single Gaussian component is implicitly taken, which is too strong. In this paper, the image patches are modeled using mixtures of probabilistic principal component analysis (MPPCA) and the corresponding MPPCANet (PCANet constructed using mixtures of probabilistic principal component analysis) is proposed. The proposed model is applied to the few-shot learning scenario. In the proposed framework, the image patches are assumed to come from several suppositions of Gaussian components. In the process of estimating the parameters of the MPPCA model, the clustering of the training image patches and the principal components of each cluster are simultaneously obtained. The number of mixture components is automatically determined during the optimization procedure. The theoretical insights of the proposed MPPCANet is elaborated by comparing with our prior work, CPCANet (PCANet with clustering-based filters). The proposed MPPCANet is evaluated on several benchmarking visual data sets in the experiment. It is compared with the original PCANet, CPCANet and several state-of-the-art methods. The experimental results show that the proposed MPPCANet has improved significantly the recognition capability of the original PCANet under the few-shot learning scenario. The performance of the MPPCANet is also better than the CPCANet in most cases. (c) 2020 Elsevier Ltd. All rights reserved.