Deep neural networks with local connectivity and its application to astronomical spectral data

The success of deep learning proves that deep models are able to achieve much better performance than shallow models in representation learning. However, deep neural networks with auto-encoder stacked structure suffer from low learning efficiency since common used training algorithms are variations of iterative algorithms based on the time-consuming gradient descent, especially when the network structure is complicated. To deal with this complicated network structure problem, we employ a “divide and conquer” strategy to design a locally connected network structure to decrease the network complexity. The basic idea of our approach is to force the basic units of the deep architecture, e.g., auto-encoders, to extract local features in an analytical way without iterative optimization and assemble these local features into a unified feature. We apply this method to process astronomical spectral data to illustrate the superiority of our approach over other baseline algorithms. Furthermore, we investigate visual interpretations of high level features and the model to demonstrate what exactly the model learn from the data.