Information Cut for Clustering using a Gradient Descent Approach (2009)
Robert Jenssen, Deniz Erdogmus, Jose C. Principe, Torbjørn Eltoft
We introduce a new graph cut for clustering which we call the Information Cut. It is derived using Parzen windowing to estimate an information theoretic distance measure between probability density...
Robert Jenssen, Jose C. Principe, Torbjørn Eltoft Uit, Kenneth Hild Ohsu, Book Chapter, Shannon Entropy
Sum-of-squared errors
The Laplacian Classifier (2009)
Robert Jenssen, Deniz Erdogmus, Jose C. Principe, Torbjørn Eltoft
Abstract—We develop a novel classifier in a kernel feature space related to the eigenspectrum of the Laplacian data matrix. The classification cost function measures the angle between class mean...
A Multi-Class Support Vector Machine Based on Scatter Criteria (2009)
Jenssen, Robert, Kloft, Marius, Zien, Alexander, Sonnenburg, Sören, Müller, Klaus-Robert
We re-visit Support Vector Machines (SVMs) and provide a novel interpretation thereof in terms of weighted class means and scatter theory. The gained theoretical insight can be translated into a...
Robert Jenssen, Deniz Erdogmus, Josec. Principefellow, Torbjørn Eltoft
Abstract — Recent work has revealed a close connection between certain information theoretic divergence measures and properties of Mercer kernel feature spaces. Specifically, it has been proposed...
Robert Jenssen, Deniz Erdogmus, Josec. Principefellow, Torbjørn Eltoft
Abstract — Recent work has revealed a close connection between certain information theoretic divergence measures and properties of Mercer kernel feature spaces. Specifically, it has been proposed...
INDEFINITE PARZEN WINDOW FOR SPECTRAL CLUSTERING ∗ (2008)
Kernel functions used to compute pairwise similarities in spectral clustering and kernel methods are almost exclusively positive semidefinite. We show that for a recent information theoretic spectral...
AN INFORMATION THEORETIC PERSPECTIVE TO KERNEL K-MEANS ∗ (2008)
Robert Jenssen, Torbjørn Eltoft
In this paper, we provide an information theoretic perspective to kernel K-means. We show that kernel K-means corresponds to maximizing an integrated squared error divergence measure between Parzen...
Information Theoretic Spectral Clustering (2008)
Robert Jenssen, Torbjørn Eltoft, Jose C. Principe
Abstract — We discuss a new information-theoretic framework for spectral clustering that is founded on the recently introduced Information Cut. A novel spectral clustering algorithm is proposed,...
Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm (2008)
Robert Jenssen, Torbjørn Eltoft, Mark Girolami, Deniz Erdogmus
We propose a new kernel-based data transformation technique. It is founded on the principle of maximum entropy (MaxEnt) preservation, hence named kernel MaxEnt. The key measure is Renyi’s entropy...
Robert Jenssen, Deniz Erdogmus, Jose C. Principe, Torbjørn Eltoft
Abstract. This paper addresses the problem of efficient information theoretic, non-parametric data clustering. We develop a procedure for adapting the cluster memberships of the data patterns, in...
Sparse Code Shrinkage Based on the Normal Inverse Gaussian Density Model (2007)
Robert Jenssen, Tor Arne Øig˚ard, Torbjørn Eltoft, Alfred Hanssen
In this paper we introduce the recent normal inverse Gaussian (NIG) probability density as a new model for sparsely coded data. The NIG density is a flexible, four-parameter density, which is highly...
Sparse Code Shrinkage Based on the Normal Inverse Gaussian Density Model (2007)
Robert Jenssen, Torbjrn Eltoft, Alfred Hanssen
In this paper we introduce the recent normal inverse Gaussian (NIG) probability density as a new model for sparsely coded data. The NIG density is a flexible, four-parameter density, which is highly...
Some Equivalences between Kernel Methods and Information Theoretic Methods (2006)
Robert Jenssen, Deniz Erdogmus, Jose C. Principe, Torbjørn Eltoft
Abstract. In this paper, we discuss some equivalences between two recently introduced statistical learning schemes, namely Mercer kernel methods and information theoretic methods. We show that Parzen...
An Information Theoretic Approach to Machine Learning (2005)
In this thesis, theory and applications of machine learning systems based on information theoretic criteria as performance measures are studied. A new clustering algorithm based on maximizing the...
The Laplacian PDF Distance: A Cost Function for Clustering in a Kernel Feature Space (2005)
Robert Jenssen, Deniz Erdogmus, Jose Principe, Torbjørn Eltoft
A new distance measure between probability density functions (pdfs) is introduced, which we refer to as the Laplacian pdf distance. The Laplacian pdf distance exhibits a remarkable connection to...
The Laplacian Spectral Classifier (2005)
Robert Jenssen, Deniz Erdogmus, Jose C. Principe, Torbjørn Eltoft
We develop a novel classifier in a kernel feature space defined by the eigenspectrum of the Laplacian data matrix. The classification cost function is derived from a distance measure between...
Eltoft: ICA filter bank for segmentation of textured images (2003)
Robert Jenssen, Torbjørn Eltoft
Independent component analysis (ICA) of textured images is presented as a computational technique for creating a new data dependent filter bank for use in texture segmentation. We show that the ICA...
For Segmentation Of, Robert Jenssen, Torbjørn Eltoft
Independent component analysis (ICA) of textured images is presented as a computational technique for creating a new data dependent filter bank for use in texture segmentation. We show that the ICA...
Sparse Code Shrinkage Based On (2001)
The Normal Inverse, Robert Jenssen, Tor Arne Øig˚ard, Torbjørn Eltoft, Alfred Hanssen
In this paper we introduce the recent normal inverse Gaussian (NIG) probability density as a new model for sparsely coded data. The NIG density is a flexible, four-parameter density, which is highly...