1 research outputs found
Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series
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.Comment: 6 pages, ESANN 2019 conferenc