Knowledge Driven Dimension Reduction For Clustering

However, most dimension reduction approaches are driven by objective functions that may not or only We will provide more detail on our solution to this problem partially suit the end users requirements. In this later but it is important to note the problem of focus in this work, we show how to incorporate general-purpose paper is different to spectral clustering (dimension reduction) domain expertise encoded as a graph into dimension in two keys ways. Firstly, we are projecting the entire space reduction in way that lends itself to an elegant D occupies not just the points in G or D. Secondly, we do generalized eigenvalue problem. We call not formulate the problem as some form of min-cut and then our approach Graph-Driven Constrained Dimension solve a relaxed version of the problem. Reduction via Linear Projection (GCDR-LP) Our work aims to find a reduced dimension space based on and show that it has several desirable properties.