Combining Real and Virtual Graphs to Enhance Data Clustering

Fusion of multiple information sources can yield significant benefits to accomplishing certain learning tasks. This paper exploits the sparse representation of signals for the problem of data clustering. The method is built within the framework of spectral clustering algorithms, which convexly combines a real graph constructed from the given physical features with a virtual graph constructed from sparse reconstructive coefficients. The experimental results on several real-world data sets have shown that fusion of both real and virtual graphs can obtain better (or at least comparable) results than using either graph alone.