Analyzing High-Dimensional Data by Subspace Validity

Amihood Amir, Reuven Kashi, Nathan S. Netanyahu, Daniel A. Keim, Markus Wawryniuk

Abstract: We are proposing a novel method that makes it possible to analyze high-dimensional data with arbitrary shaped projected clusters and high noise levels. At the core of our method lies the idea of subspace validity. We map the data in a way that allows us to test the quality of subspaces using statistical tests. Experimental results, both on synthetic and real data sets, demonstrate the potential of our method.