Patent ID: 7724961

Claim:
A computer implemented method for constructing a classifier for classifying test data, comprising the steps of: generating high-level features from low-level features extracted from training data, the high-level features being positive definite matrices in a form of an analytical manifold; selecting a subset of the high-level features; determining an intrinsic mean matrix from the selected subset of the high-level features; mapping each high-level feature to a feature vector onto a tangent space of the analytical manifold using the intrinsic mean matrix; and training an untrained classifier model with the feature vector to obtain a trained classifier, wherein the determining, mapping, and training steps are performed for a plurality of selected subsets of the high-level features and a plurality of untrained classifier models: and further comprising: assigning a weight to each feature vector during the training step; measuring a performance of each trained classifier; and selecting the trained classifier having a best performance for inclusion in a boosted classifier.