Metrics of grassmannian representation in reproducing kernel hilbert space for variational pattern analysis

Variation of patterns in signal can be represented by the covariance structure of vectors or its eigensubspace. When information of the pattern variation is available, representation by the covariance matrix or the eigensubspace is useful for feature extraction and classification compared with standard vector or matrix representations.