Text Categorization via Ellipsoid Separation
We present a new batch learning algorithm for text classification in the vector space of document representations. The algorithm uses ellipsoid separation in the feature space which leads to a semidefinite program. An approximation of the latent semantic feature extraction approach using Gram-Schmidt orthogonalization is used for the feature extraction. Preliminary results demonstrate some potential for the presented approach.