In this paper, we propose a method of improving Convolutional Neural Networks (CNN) by determining the optimal alignment of weights and inputs using dynamic programming. Conventional CNNs convolve learnable shared weights, or filters, across the input data. These filters use an inner product to linearly match the shared weights to a window of the input. However, it is possible that there exists a more optimal alignment of weights. Thus, we propose the use of Dynamic Time Warping (DTW) to dynamically align the weights to optimized input elements. This dynamic alignment is especially useful for time series recognition due to the complexities with temporal distortions, such as varying rates and sequence lengths. We demonstrate the effectiveness of the proposed architecture on the Unipen online handwritten digit and character datasets, the UCI Spoken Arabic Digit dataset, and the UCI Activities of Daily Life dataset.
DTW-NNs @cite_3 similarly use DTW as a nonlinear inner product for neural networks. The difference between a DTW-NN and the proposed model is that DTW-NN uses a sum of Euclidean distances between a learned prototype sequence and the input. This means that the values of the weights are determined by a distance metric and not directly trained like the method described in this paper. In addition, we implement a CNN with shared weights which means that the output of the node retains temporal qualities instead of a scalar output. This allows for the ability to stack multiple dynamically aligned weight layers to create deep networks.
@cite_3
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Temporal pattern recognition is challenging because temporal patterns require extra considerations over other data types, such as order, structure, and temporal distortions. Recently, there has been a trend in using large data and deep learning, however, many of the tools cannot be directly used with temporal patterns. Convolutional Neural Networks (CNN) for instance are traditionally used for visual and image pattern recognition. This paper proposes a method using a neural network to classify isolated temporal patterns directly. The proposed method uses dynamic time warping (DTW) as a kernel-like function to learn dissimilarity-based feature maps as the basis of the network. We show that using the proposed DTW-NN, efficient classification of on-line handwritten digits is possible with accuracies comparable to state-of-the-art methods.