Time: 2018-12-05 10：00-12：00

Venue: Room 203 Lecture Hall

abstract：Hyperspectral imaging has been of great interest in remote sensing and Earth observations due to the fact that its high spectral resolution offers powerful discriminant capability in separating objects or materials with subtle spectral discrepancy. However, the resulting high spectral dimensionality may bring out difficulty in data processing and analysis. Traditional clustering techniques, such as k-means clustering, may not work well for high-dimensional data because distance measurement becomes less accurate in such a case. Sparse subspace clustering is more suitable for high-dimensional data clustering which is to cluster data points that lie in a union of low-dimensional subspaces. The key idea is to find a sparse representation of a data point (in terms of other points), which corresponds to selecting a few points from the same subspace. The solution is used in a spectral clustering framework to infer the clustering of the data into subspaces. In this talk, the original sparse subspace clustering, low-rank subspace clustering, low-rank and sparse subspace clustering, and its multiview versions are introduced. Moreover, their kernel extension and approximate kernel extension are also discussed. Comparison with state-of-the-arts in terms of unsupervised classification accuracy is presented to demonstrate the superiority of multiview versions. Existing challenges will also be discussed.