Unsupervised Deep Transform Learning

Abstract

We introduce deep transform learning - a new tool for deep learning. Deeper representation is learnt by stacking one transform after another. The model is akin to a feedforward neural network. The first layer learns the transform and features from the input training samples. Subsequent layers use the features (after activation) from the previous layers as training input. However, this explanation is only given for intuitive understanding; the ensuing problem is not solved in a greedy fashion. All the layers are solved jointly. Experiments have been carried out with other state-of-the-art. Results on classification and clustering show that our proposed technique is better than all the said techniques, at least on the benchmark datasets compared on.