Robust Learning from Ortho-Diffusion Decompositions

Sravan Gudivada, Adrian G. Bors

Abstract: This paper describes a new classification method based on modeling data by embedding diffusions into orthonormal decompositions of graph-based data representations. The training data is represented by an adjacency matrix calculated using either the correlation or the covariance of the training set. The application of the modified Gram-Schmidt orthonormal decomposition alternating with diffusion and data reduction stages, is applied recursively at each scale level. The diffusion process is strengthening the representation pattern of representative features. Meanwhile, noise is removed together with non-essential detail during the data reduction stage. The proposed methodology is shown to be robust when applied to face recognition considering low image resolution and corruption by various types of noise.