A projection and density estimation method for knowledge discovery.
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.
Technical University Berlin, Computer Vision and Remote Sensing Group, Franklinstr, Berlin, Germany. stanski@mailbox.tu-berlin.de
Adam
A
Stanski
Olaf
O
Hellwich
D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D015233:Models, Statistical; D011336:Probability