K-Subspaces for Sequential Data
Published In
2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Document Type
Citation
Publication Date
2023
Abstract
We study the problem of clustering high-dimensional temporal data such as video sequences of human motion, where points that arrive sequentially in time are likely to belong to the same cluster. State-of-the-art approaches to this problem rely on the union-of-subspaces model, where points lie near one of $K$ unknown low-dimensional subspaces. We propose the first approach to sequential subspace clustering based on the popular $K$-Subspaces (KSS) formulation, which we refer to as Temporal $K$-Subspaces (TKSS). We show how sequential information can be incorporated into the KSS problem and provide an efficient algorithm for approximate minimization of the resulting cost function, proving convergence to a local minimum. Results on benchmark datasets show that TKSS achieves state-of-the-art performance, obtaining an accuracy increase of over 10% compared to existing methods.
Locate the Document
DOI
10.1109/CAMSAP58249.2023.10403417
Persistent Identifier
https://archives.pdx.edu/ds/psu/41221
Publisher
IEEE
Citation Details
Sheng, W., & Lipor, J. (2023, December 10). K-Subspaces for Sequential Data. 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). https://doi.org/10.1109/camsap58249.2023.10403417