A Randomized Approach to Sparse Subspace Clustering using Spectral clustering

The steps taken to segment an in-motion object from its training set is a major feature in a lot of computer vision applications ranging from motion segmentation to image recognition. A random subspace is expressed into sparse representation called Randomized Sparse Subspace Clustering (RSSC), which is capable of intensifying the precision of the subspace cluster on real-life datasets that we tested on significantly. RSSC adopts the assumption that high-dimensional data actually lie on the low-dimensional manifold such that out-of-sample data could be grouped in the neighboring space learned from in-sample data. Experimental results show that RSSC is potential in clustering out-of-sample data