a projection and density estimation method for knowledge discovery知识发现的投影和密度估计方法.pdfVIP

A Projection and Density Estimation Method for Knowledge Discovery Adam Stanski*, Olaf Hellwich Technical University Berlin, Computer Vision and Remote Sensing Group, Franklinstr. Berlin, Germany Abstract A key ingredient to modern data analysis is probability density estimation. However, it is well known that the curse of dimensionality prevents a proper estimation of densities in high dimensions. The problem is typically circumvented by using a fixed set of assumptions about the data, e.g., by assuming partial independence of features, data on a manifold or a customized kernel. These fixed assumptions limit the applicability of a method. In this paper we propose a framework that uses a flexible set of assumptions instead. It allows to tailor a model to various problems by means of 1d-decompositions. The approach achieves a fast runtime and is not limited by the curse of dimensionality as all estimations are performed in 1d-space. The wide range of applications is demonstrated at two very different real world examples. The first is a data mining software that allows the fully automatic discovery of patterns. The software is publicly available for evaluation. As a second example an image segmentation method is realized. It achieves state of the art performance on a benchmark dataset although it uses only a fraction of the training data and very simple features. Citation: Stanski A, Hellwich O (2012) A Projection and Density Estimation Method for Knowledge Discovery. PLoS ONE 7(10): e44495. doi:10.1371/ journal.pone.0044495 Editor: Ioannis P. Androulakis, Rutgers University, United States of America Received March 30, 2012; Accepted August 3, 2012; Published October 1, 2012 Copyright: 2012 Stanski, Hellwich. This is an open-access article distributed under the terms of the Creative Commons At