| مستخلص: |
Long short-term memory (LSTM) network is widely applied to multi-dimensional time series modeling to solve many real-world problems, and visual analytics plays a crucial role in improving its interpretability. To understand the high-dimensional activations in the hidden layer of the model, the application of dimensionality reduction (DR) techniques is essential. However, the diversity of DR techniques dramatically increases the difficulty of selecting one among them. In this paper, aiming at the applicability of DR techniques for visual analysis of LSTM hidden activity on multi-dimensional time series modeling, we select four representative DR techniques as the comparison objects, including principal component analysis (PCA), multi-dimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP). The original continuous modeling data and the symbolically processed discrete data are used as knowledge of model learning, which are associated with LSTM hidden layer activity, and the ability of DR techniques to maintain high-dimensional information of the hidden layer activation is compared. According to the model structure of LSTM and the characteristics of modeling data, the controlled experiments were carried out in five typical tasks, namely the quality evaluation of DR, the abstract representation of high and low hidden layers, the association analysis between model and output variable, the importance analysis of input features and the exploration of temporal regularity. Through the complete experimental process and detailed result analysis, we distilled a systematic guidance for analysts to select appropriate and effective DR techniques for visual analytics of LSTM. [ABSTRACT FROM AUTHOR] |
