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By combining convolutional neural networks (CNN) and long short term memory networks (LSTM) into the learning structure, this paper presents a supervised learning method called combining deep neural networks (CDNN) for protein secondary structure prediction. First, we use multiple convolutional neural networks with different number of layers and different size of filters to extract the protein secondary structure features. Second, we use bidirectional LSTM to extract features continually based on the raw features and the features which extracted by CNNs. Third, a fully connected dense layer is used to map the extracted features by LSTM to the different protein secondary structure classes. CDNN architecture is trained by RMSProp optimizer based on the cross entropy error between protein secondary structure labels and dense layer's outputs. CDNN not only inherits the abstraction ability of CNN and sequence data process ability of LSTM, but also demonstrates the attractive classification ability for handling protein secondary structure data. The empirical validation on two protein secondary structure prediction datasets demonstrates the effectiveness of CDNN method.

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Image classification is an important problem in computer vision. The sparse coding spatial pyramid matching (ScSPM) framework is widely used in this field. However, the sparse coding cannot effectively handle very large training sets because of its high computational complexity, and ignoring the mutual dependence among local features results in highly variable sparse codes even for similar features. To overcome the shortcomings of previous sparse coding algorithm, we present an image classification method, which replaces the sparse dictionary with a stable dictionary learned via low computational complexity clustering, more specifically, a k-medoids cluster method optimized by k-means++. The proposed method can reduce the learning complexity and improve the feature’s stability. In the experiments, we compared the effectiveness of our method with the existing ScSPM method and its improved versions. We evaluated our approach on two diverse datasets: Caltech-101 and UIUC-Sports. The results show that our method can increase the accuracy of spatial pyramid matching, which suggests that our method is capable of improving performance of sparse coding features.

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