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Subspace Clustering for Sequential Data (2014)
| Content Provider | CiteSeerX |
|---|---|
| Author | Tierney, Stephen Gao, Junbin Guo, Yi |
| Description | We propose Ordered Subspace Clustering (OSC) to seg-ment data drawn from a sequentially ordered union of sub-spaces. Current subspace clustering techniques learn the relationships within a set of data and then use a separate clustering algorithm such as NCut for final segmentation. In contrast our technique, under certain conditions, is ca-pable of segmenting clusters intrinsically without providing the number of clusters as a parameter. Similar to Sparse Subspace Clustering (SSC) we formulate the problem as one of finding a sparse representation but include a new penalty term to take care of sequential data. We test our method on data drawn from infrared hyper spectral data, video sequences and face images. Our experiments show that our method, OSC, outperforms the state of the art meth-ods: Spatial Subspace Clustering (SpatSC), Low-Rank Rep-resentation (LRR) and SSC. |
| File Format | |
| Language | English |
| Publisher Date | 2014-01-01 |
| Publisher Institution | In Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition |
| Access Restriction | Open |
| Subject Keyword | Art Meth-ods Seg-ment Data Drawn Spatial Subspace Clustering Face Image Low-rank Rep-resentation Video Sequence New Penalty Term Current Subspace Final Segmentation Sequential Data Sparse Representation Ordered Subspace Clustering Certain Condition Infrared Hyper Spectral Data Separate Clustering Algorithm Subspace Clustering |
| Content Type | Text |
| Resource Type | Article |
