Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series
This addresses the challenge of handling unseen MTS classes in domains like motion analysis, though it appears incremental as it builds on existing dictionary learning and kernel methods.
The paper tackles the problem of reconstructing and clustering unseen multivariate time-series (MTS) classes, such as motion data, by proposing a multiple-kernel dictionary learning method that achieves interpretable reconstruction and high performance in online clustering on real benchmarks.
There exist many approaches for description and recognition of unseen classes in datasets. Nevertheless, it becomes a challenging problem when we deal with multivariate time-series (MTS) (e.g., motion data), where we cannot apply the vectorial algorithms directly to the inputs. In this work, we propose a novel multiple-kernel dictionary learning (MKD) which learns semantic attributes based on specific combinations of MTS dimensions in the feature space. Hence, MKD can fully/partially reconstructs the unseen classes based on the training data (seen classes). Furthermore, we obtain sparse encodings for unseen classes based on the learned MKD attributes, and upon which we propose a simple but effective incremental clustering algorithm to categorize the unseen MTS classes in an unsupervised way. According to the empirical evaluation of our MKD framework on real benchmarks, it provides an interpretable reconstruction of unseen MTS data as well as a high performance regarding their online clustering.