Classification model with collinear grouping of features

Abstract

ABSTRACTPattern recognition procedures operate on data represented as sets of multidimensional feature vectors. A small sample of data appears when the dimension of the feature vectors (number of features) is much larger than the number of feature vectors (objects). Small datasets often emerge in practice, for example in genetics. The design of classification or prognostic models on small data sets requires the development of new types of methods. Methods based on L1 geometry can play an important role in this regard.

Keywords

• Small data sets
• multidimensional feature vectors
• maximizing L1 norm margins
• convex and piecewise linear criterion functions