I have been using PLSA to reduce the dimension of image BOV models due to its ability to capture the co-occurrence of visual-words. However,  the linearity of PLSA limits its accuracy when applied to the classification of image collections with high and medium complexity. I have recently discovered that Deep Learning algorithms provides non-linear approaches for machine learning tasks. which of the deep learning approaches is the most suitable for dimension reduction?

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