Overfitting in linear feature extraction for classification of high-dimensional image data
The causes of over-fitting in feature extraction for high-dimensional datasets are revealed. We prove the theoretical existence of perfectly discriminative subspace projections. Direct, inverse relationship between the classification performance the levels of inter-class discrimination. Soft Discriminant Maps consistently performs better than other comparable techniques.
overfitting feature extraction classification high-dimensional datasets dimensionality reduction