Title: Linear centralization classifier,
Abstract: A classification algorithm, called the Linear Centralization Classifier
(LCC), is introduced. The algorithm seeks to find a transformation that best
maps instances from the feature space to a space where they concentrate towards
the center of their own classes, while maximimizing the distance between class
centers. We formulate the classifier as a quadratic program with quadratic
constraints. We then simplify this formulation to a linear program that can be
solved effectively using a linear programming solver (e.g., simplex-dual). We
extend the formulation for LCC to enable the use of kernel functions for
non-linear classification applications. We compare our method with two standard
classification methods (support vector machine and linear discriminant
analysis) and four state-of-the-art classification methods when they are
applied to eight standard classification datasets. Our experimental results
show that LCC is able to classify instances more accurately (based on the area
under the receiver operating characteristic) in comparison to other tested
methods on the chosen datasets. We also report the results for LCC with a
particular kernel to solve for synthetic non-linear classification problems.