Generalizing Long Short-Term Memory Network for Deep Learning from Generic Data

Abstract

Long Short-Term Memory (LSTM) network, a popular deep-learning model, is particularly useful for data with temporal correlation, such as texts, sequences, or time series data, thanks to its well-sought after recurrent network structures designed to capture temporal correlation. In this article, we propose to generalize LSTM to generic machine-learning tasks where data used for training do not have explicit temporal or sequential correlation. Our theme is to explore feature correlation in the original data and convert each instance into a synthetic sentence format by using a two-gram probabilistic language model. More specifically, for each instance represented in the original feature space, our conversion first seeks to horizontally align original features into a sequentially correlated feature vector, resembling to the letter coherence within a word. In addition, a vertical alignment is also carried out to create multiple time points and simulate word sequential order in a sentence ( i.e., word correlation). The two dimensional horizontal-and-vertical alignments not only ensure feature correlations are maximally utilized, but also preserve the original feature values in the new representation. As a result, LSTM model can be utilized to achieve good classification accuracy, even if the underlying data do not have temporal or sequential dependency. Experiments on 20 generic datasets show that applying LSTM to generic data can improve the classification accuracy, compared to conventional machine-learning methods. This research opens a new opportunity for LSTM deep learning to be broadly applied to generic machine-learning tasks.