Multi-View, Generative, Transfer Learning for Distributed Time Series Classification

In this paper, we propose an effective, multi-view, generative, transfer learning framework for multivariate time series data. While generative models are demonstrated effective for several machine learning tasks, their application to time-series classification problems is underexplored. The need for additional exploration is motivated when data are large, annotations are unbalanced or scarce, or data are distributed and fragmented. Recent advances in computer vision attempt to use synthesized samples with system generated annotations to overcome the lack or imbalance of annotated data. However, in multi-view problem settings, view mismatches between the synthetic data and real data pose additional challenges against harnessing new annotated data collections. The proposed method offers important contributions to facilitate knowledge sharing, while simultaneously ensuring an effective solution for domain-specific, fine level categorizations. We propose a principled way to perform view adaptation in a cross-view learning environment, wherein pairwise view similarity is identified by a smaller subset of source samples that closely resemble the target data patterns. This approach integrates generative models within a deep classification framework to minimize the gap between source and target data. More precisely, we design category specific conditional, generative models to update the source generator in order for transforming source features so that they appear as target features and simultaneously tune the associated discriminative model to distinguish these features. During each learning iteration, the source generator is conditioned by a source training set represented as some target-like features. This transformation in appearance was performed via a target generator specifically learned for target-specific customization per category. Afterward, a smaller source training set, indicating close target pattern resemblance in terms of the corresponding generative and discriminative loss, is used to fine-tune the source classification model parameters. Experiments show that compared to existing approaches, our proposed multi-view, generative, transfer learning framework improves time series classification performance by around 4% in the UCI multi view activity recognition dataset, while also showing a robust, generalized representation capacity in classifying several large-scale multi-view light curve collections.