Agglomerative independent variable group analysis

Agglomerative Independent Variable Group Analysis

Independent Variable Group Analysis (IVGA) is a principle for grouping dependent variables together while keeping mutually independent or weakly dependent variables in separate groups. In this paper an agglomerative method for learning a hierarchy of IVGA groupings is presented. The method resembles hierarchical clustering, but the distance measure is based on a model-based approximation of mut...

Independent Variable Group Analysis

MultiDendrograms: Variable-Group Agglomerative Hierarchical Clusterings

MultiDendrograms is a Java-written application that computes agglomerative hierarchical clusterings of data. Starting from a distances (or weights) matrix, MultiDendrograms is able to calculate its dendrograms using the most common agglomerative hierarchical clustering methods. The application implements a variable-group algorithm that solves the non-uniqueness problem found in the standard pai...

Independent Variable Group Analysis in Learning Compact Representations for Data

Humans tend to group together related properties in order to understand complex phenomena. When modeling large problems with limited representational resources, it is important to be able to construct compact models of the data. Structuring the problem into sub-problems that can be modeled independently is a means for achieving compactness. We describe the Independent Variable Group Analysis (I...

Input variable selection using independent component analysis

The problem of input variable selection is well known in the task of modeling real world data. In this paper, we propose a novel model-free algorithm for input variable selection using independent component analysis and higher order cross statistics. Experimental results are given which indicate that the method is capable of giving reliable performance and that it outperforms other approaches w...