DeLTa: Deep local pattern representation for time-series clustering and classification using visual perception

• Published in: Knowledge-based systems Vol. 212; p. 106551
• Main Authors: Anand, Gaurangi, Nayak, Richi
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 05.01.2021; Elsevier Science Ltd
• Subjects: Algorithms; Artificial neural networks; Clustering; Cognitive tasks; Convolutional Neural Network; Datasets; Deep learning; Empirical analysis; Internet of Things; Layouts; Machine learning; Matching; Object recognition; Representations; Robotics; Source code; Time series; Time-series classification; Time-series clustering; Transformations (mathematics); Unsupervised feature representation; Visual perception; Deep learning; Convolutional Neural Network; Time-series; Time-series clustering; Unsupervised feature representation; Time-series classification
• ISSN: 0950-7051; 1872-7409; 1872-7409
• DOI: 10.1016/j.knosys.2020.106551

Time-series analysis is of enormous significance to a multitude of domains such as Internet-of-Things (IoT), prognostics, health, and robotics. Machine learning tasks require time-series data in the form of features for the application of (un)supervised algorithms. The existing feature representation methods lack generality and are domain-specific, especially those based on supervised learning. In this paper, we propose a novel time-series feature representation method based on feature transformation and feature learning. The feature transformation process is inspired by the human cognitive thinking used in visual recognition, where the 1-D time-series data is transformed into a 2-D image dataset. A feature set is learned by imposing a pre-trained convolutional neural network on the transformed search space. This generates two complementary high-dimensional feature sets: (1) one with the matching of the overall 2-D layout of the time-series; and (2) another with matching based on the activation of the local 2-D patterns irrespective of the overall layout. Empirical analysis on a large number of benchmark datasets shows the advantage of the domain-agnostic nature of DeLTa in achieving higher accuracy in comparison to relevant benchmarking methods. Source code is publicly available at https://github.com/technophyte/DeLTa.