To apply unsupervised feature learning to emotional semantic analysis for images in small sample size situations, convolutional sparse autoencoder based self-taught learning for domain adaption is adopted for affective classification of a small amount of labeled abstract images. To visually compare the results of feature learning on different domains, an average gradient criterion based method is further proposed for the sorting of weights learned by sparse autoencoders. Image patches are first randomly col...
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