Nearest-Instance-Centroid-Estimation Linear Discriminant Analysis (Nice Lda)

Abstract

We propose a novel cascaded classification technique called the Nearest Instance Centroid Estimation (NICE) LDA algorithm. Our algorithm (inspired from NICE KLMS) performs a cascade combination of two weak classifiers - threshold based class-wise clustering and linear discriminant classification to achieve state-of-the-art results on various high dimensional UCI datasets. We show how our method is more robust towards skewed data and computationally more efficient than previous methods of combining clustering with classification techniques. We also develop an efficient aggregation method based on instance based learning that implements this cascade combination of classifiers in a much simpler manner computationally. We demonstrate that our method of data clustering and LDA implementation, while introducing only one free parameter, leads to results that are similar and often better than those achieved by the state-of-the-art kernel RBF SVMs.