Publication

Abstract : Time-Series Classification (TSC) has gained substantial importance in applications such as healthcare, finance, manufacturing, and human activity recognition. Training and evaluating a model on data with diverse distributions pose challenges across domains, but Unsupervised Domain Adaptation (UDA) proves efficient in mitigating these distribution discrepancies. Current TSC-UDA methods exploit statistical, adversarial, or contrastive learning strategies. Even though contrastive learning is an emerging UDA, relying solely on it proves inadequate in addressing the non-linear characteristics of the non-stationary multivariate temporal data. Kernelization enables better capturing of these non-linear relationships among the samples. Hence, an optimal kernel is derived by applying multiple kernel variants to samples and the maximum mean discrepancy metric is computed. The augmented samples in contrastive learning can result in overfitting, hence L 1 n o r m regularization is adopted to eliminate it. Therefore, we introduce a comprehensive approach Multikernel coNtrastivE doMain adaptatiON for tIme-series Classification ( MNEMONIC ) that embraces statistical feature alignment, contrastive learning, and regularization. Consequently, the model learns the inherent relationship among non-monotonic temporal data points and aligns the domains into a shared subspace, reducing the distribution differences. Extensive experimental evaluation is performed and the results are compared to the state-of-the-art with a macro-F1 score of 94.36%, 86.19%, 70.08%, and 63.07% for UCIHAR, HHAR, WISDM, and SSC datasets respectively.