Enabling Tensor Decomposition for Time-Series Classification via A Simple Pseudo-Laplacian Contrast
This addresses a domain-specific problem for researchers in time-series analysis by providing an incremental improvement over existing tensor decomposition methods.
The paper tackles the problem of adapting tensor decomposition for time-series classification by proposing a Pseudo Laplacian Contrast framework, which enables extraction of class-aware representations while preserving low-rank structure, achieving effective results across various datasets.
Tensor decomposition has emerged as a prominent technique to learn low-dimensional representation under the supervision of reconstruction error, primarily benefiting data inference tasks like completion and imputation, but not classification task. We argue that the non-uniqueness and rotation invariance of tensor decomposition allow us to identify the directions with largest class-variability and simple graph Laplacian can effectively achieve this objective. Therefore we propose a novel Pseudo Laplacian Contrast (PLC) tensor decomposition framework, which integrates the data augmentation and cross-view Laplacian to enable the extraction of class-aware representations while effectively capturing the intrinsic low-rank structure within reconstruction constraint. An unsupervised alternative optimization algorithm is further developed to iteratively estimate the pseudo graph and minimize the loss using Alternating Least Square (ALS). Extensive experimental results on various datasets demonstrate the effectiveness of our approach.