Generalized Time Warping Invariant Dictionary Learning for Time Series Classification and Clustering
This work addresses temporal misalignment problems in time series analysis for applications like pattern recognition, but it is incremental as it builds on existing dictionary learning and DTW techniques.
The paper tackles the overfitting and information loss issues in dynamic time warping (DTW) for time series classification and clustering by proposing a generalized time warping invariant dictionary learning algorithm with a continuous temporal warping operator. The method shows superiority over benchmarks on ten public datasets.
Dictionary learning is an effective tool for pattern recognition and classification of time series data. Among various dictionary learning techniques, the dynamic time warping (DTW) is commonly used for dealing with temporal delays, scaling, transformation, and many other kinds of temporal misalignments issues. However, the DTW suffers overfitting or information loss due to its discrete nature in aligning time series data. To address this issue, we propose a generalized time warping invariant dictionary learning algorithm in this paper. Our approach features a generalized time warping operator, which consists of linear combinations of continuous basis functions for facilitating continuous temporal warping. The integration of the proposed operator and the dictionary learning is formulated as an optimization problem, where the block coordinate descent method is employed to jointly optimize warping paths, dictionaries, and sparseness coefficients. The optimized results are then used as hyperspace distance measures to feed classification and clustering algorithms. The superiority of the proposed method in terms of dictionary learning, classification, and clustering is validated through ten sets of public datasets in comparing with various benchmark methods.