SOMNet: Unsupervised Feature Learning Networks for Image Classification
Richard Hankins, Yao Peng, Hujun Yin · 2018
We present here an unsupervised approach to learning suitable features for a deep learning framework applied to image classification. PCANet was introduced as a simple and efficient baseline for deep learning approaches which used cascaded principle component analysis (PCA) derived filter banks, as well as other simple image processing elements such as binary hashing and blockwise histograms. This was followed by DCTNet which used discrete cosine transform (DCT) filter banks as a learning-free alternative. In this paper we propose SOMNet which uses self-organizing map (SOM) based filters offering a non-orthogonal alternative to PCANet providing comparable performance. It is well established that SOM is a non-linear version of PCA but does not suffer from the same constraints. We also show that through the use of a simple trick in the binarization process results in a dramatic reduction in the dimension of the final feature vector, thus allowing the utilization of more filters which could lead to deeper and more complex structures in further work. We also demonstrate the results of a hybrid methodology that clusters generative Markov random fields (MRF) as filters which provides more diverse features in a data driven approach to deep learning.