Short Sequence Classification Through Discriminable Linear Dynamical System

Linear dynamical system (LDS) offers a convenient way to reveal the unobservable structure behind the data. This makes it useful for data representation and explanatory analysis. An immediate limitation with this model is that most training algorithms train a model to best approximate a sequential instance. They do not consider its class or label which indicates the dissimilarity/similarity to other instances. As a result, LDS's trained in this way are inclined to be indistinguishable over classes, resulting in a poor performance in the model-based classification. In this paper, after revisiting this limitation, we propose to promote the diversity between the two models of different classes. The diversity, measured by determinantal point process (DPP) on LDS's, is utilized to remedy the greedy behavior of the electromagnetic algorithm. The training goal is a model that balances the goodness of fit and being distinguishable over classes. Experiments on synthetic data confirm its effectiveness in generating discriminative systems under supervisory information. The classification on short time-span data sets confirms that the models generated by our approach could generalize well to unseen data.