Projection Pursuit for Exploratory Supervised Classification

Projection Pursuit for Exploratory Supervised Classification

In high-dimensional data, one often seeks a few interesting low-dimensional projections that reveal important features of the data. Projection pursuit is a procedure for searching high-dimensional data for interesting low-dimensional projections via the optimization of a criterion function called the projection pursuit index. Very few projection pursuit indices incorporate class or group inform...

Localized Exploratory Projection Pursuit

Based on CART, we introduce a recursive partitioning method for high dimensional space which partitions the data using low dimensional features. The low dimensional features are extracted via an exploratory projection pursuit (EPP) method, localized to each node in the tree. In addition, we present an exploratory splitting rule that is potentially less biased to the training data. This leads to...

Exploratory basis pursuit classification

Feature selection is a fundamental process in many classifier design problems. However, it is NP-complete and approximate approaches often require requires extensive exploration and evaluation. This paper describes a novel approach that represents feature selection as a continuous regularization problem which has a single, global minimum, where the model s complexity is measured using a 1-norm ...

Feature Extraction with Supervised Projection Pursuit

Multi-Objective Genetic Programming Projection Pursuit for Exploratory Data Modeling

For classification problems, feature extraction is a crucial process which aims to find a suitable data representation that increases the performance of the machine learning algorithm. According to the curse of dimensionality [4] theorem, the number of samples needed for a classification task increases exponentially as the number of dimensions (variables, features) increases. On the other hand,...