Learning representations for object classification using multi-stage optimal component analysis

Learning data representations is a fundamental challenge in modeling neural processes and plays an important role in applications such as object recognition. Optimal component analysis (OCA) formulates the problem in the framework of optimization on a Grassmann manifold and a stochastic gradient method is used to estimate the optimal basis. OCA has been successfully applied to image classification problems arising in a variety of contexts. However, as the search space is typically very high dimensional, OCA optimization often requires expensive computational cost. In multi-stage OCA, we first hierarchically project the data onto several low-dimensional subspaces using standard techniques, then OCA learning is performed hierarchically from the lowest to the highest levels to learn about a subspace that is optimal for data discrimination based on the K-nearest neighbor classifier. One of the main advantages of multi-stage OCA lies in the fact that it greatly improves the computational efficiency of the OCA learning algorithm without sacrificing the recognition performance, thus enhancing its applicability to practical problems. In addition to the nearest neighbor classifier, we illustrate the effectiveness of the learned representations on object classification used in conjunction with classifiers such as neural networks and support vector machines.