A comparison of linear discriminant analysis and ridge classifier on Twitter data

This document is about the accuracy analysis of two of the most prominent classifiers present in today's academic arena. Classifiers are being used extensively in machine learning applications today and need to present a high rate of success to be considered useful. Tikhonov regularization incorporated within the Ridge Classifier is the basis for its classification. It utilises the LevenbergMarquardt algorithm for non-linear least-squares problems to classify objects. Linear Discriminant Analysis, on the other hand, utilises aspects of ANOVA[2,3] and regression analysis. LDA works by getting explicit information from the user. It needs the definition of the variables — both dependent and independent. It doesn't use any implicit assumptions in its modelling. There is no interconnection between the two variables initially. Using these two classifiers we compare their effectiveness at mapping a set of data scraped in real-time from Twitter to its corresponding generalised hashtag, and suggest why the differences, if any, arise.