Automatic Motion Segmentation via a Cumulative Kernel Representation and Spectral Clustering. Oña-Rocha, O., R., Sánchez-Manosalvas, O., T., Umaquinga-Criollo, A., C., Rosero-Montalvo, P., D., Suárez-Zambrano, L., E., Rodríguez-Sotelo, J., L., & Peluffo-Ordóñez, D., H. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 406-414. 2017. ![Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex Dynamic or time-varying data analysis is of great interest in emerging and challenging research on automation and machine learning topics. In particular, motion segmentation is a key stage in the design of dynamic data analysis systems. Despite several studies have addressed this issue, there still does not exist a final solution highly compatible with subsequent clustering/classification tasks. In this work, we propose a motion segmentation compatible with kernel spectral clustering (KSC), here termed KSC-MS, which is based on multiple kernel learning and variable ranking approaches. Proposed KSC-MS is able to automatically segment movements within a dynamic framework while providing robustness to noisy environments.
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