Classification of high-dimensional data using the Sparse Matrix Transform
Leonardo R. Bachega, Charles A. Bouman · 2010
In this paper, we develop a classification method for high-dimensional data based on the Sparse Matrix Transform (SMT). The recently proposed SMT has been shown to produce more accurate estimates of covariance matrices when the number of training samples n is much less than the number of dimensions p of the data. Here we introduce a classifier that uses the SMT to model the covariance structure of the data. Experiments in face recognition using the FERET face database show that our method is superior to a conceptually very similar and low-dimensional method in at least two key aspects: First, the SMT classifier is more robust to the size of the training set, remaining accurate even when only a few training samples are available; Second, the total computation required to apply the SMT classifier to high-dimensional data is very low, making this method attractive for use in low-power and mobile devices, or in application settings requiring fast computation.