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?