Low rank sequential subspace clustering

Yi Guo, Junbin Gao, Feng Li, Stephen Tierney, Ming Yin

Research output: Book chapter/Published conference paperConference paper

3 Citations (Scopus)
4 Downloads (Pure)


Sequential data are ubiquitous in data analysis. For example hyperspectral data taken from a drill hole in geology, high throughput X-ray diffraction measurements in materials research and EEG brain wave signals in neuroscience. The common feature of sequential data is that they are all acquired subject to one external variable such as location, time or temperature. The data evolve along the direction of that variable through several patterns and the 'neighboring' data are very likely to share similar features. The purpose of the segmentation for sequential data is then to identify those sequentially continuous segments/patterns. We approach this problem by adopting the subspace clustering method and propose a novel algorithm called low rank sequential subspace clustering (LRSSC), inspired by another method called spatial subspace clustering (SpatSC). SpatSC finds the subspaces by data self-reconstruction with a sparsity constraint on reconstruction weights and promotes the spatial smoothness of the weights by fusion, the essential part in the fused LASSO. However, the subspace identification capability is limited due to the indeterminacy of the sparse regression in finding suitable samples to linearly reconstruct a given sample. This confuses the graph cut algorithm that produces the final clustering results on the weights. To overcome this drawback, we propose to use the low rank penalty instead of sparsity in learning phase to separate subspaces. This improves the subspace identification as well as the robustness to noise. To demonstrate its effectiveness, we test LRSSC on both simulated and real world data compared with SpatSC and other methods. The proposed algorithm is superior to others when noise level is very high.
Original languageEnglish
Title of host publicationProceedings of the 2015 International Joint Conference on Neural Networks (IJCNN)
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Print)9781479919611
Publication statusPublished - 2015
EventIEEE International Joint Conference on Neural Networks: IJCNN 2015 - Killarney Convention Centre, Killarney, Ireland
Duration: 12 Jul 201517 Jul 2015
https://web.archive.org/web/20150429110337/http://www.ijcnn.org:80/ (Archived page)


ConferenceIEEE International Joint Conference on Neural Networks
OtherJCNN 2015 is currently scheduled to be held at the Killarney Convention Center in Killarney, Ireland on July 12-17, 2015. It will feature invited plenary talks by world-renowned speakers in the areas of neural network theory and applications, computational neuroscience, robotics, and distributed intelligence. In addition to regular technical sessions with oral and poster presentations, the conference program will include special sessions, competitions, tutorials and workshops on topics of current interest.
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    Guo, Y., Gao, J., Li, F., Tierney, S., & Yin, M. (2015). Low rank sequential subspace clustering. In Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2015.7280328