Image classification is an area where deep learning and especially stacked Auto-encoders have really proven theirstrength. The contributions of this paper lie in the creation of a new classifier to remedy some classification problems.This new method of classification presents a combination of the most used techniques in Deep Learning (DL) and SparseCoding (SC) in the field of classification. Proposed deep neural networks consist of three stacked Auto-encoders and aSoftmax used as an outer layer for classification. The first Auto-encoder is created from a sparse representation of allimages of the dataset. The sparse representation of all images represents the decoder part of the first Auto-encoder. Thenthe transpose of the matrix is applied to get the encoder part. Experiments performed on standard datasets such asImageNet and the Coil-100 reveal the efficacy of this approach.
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