The international classification of functioning, disability and health in clinical practice, research findings and their impact on training and education.

At the ICF Research Institute (at MSH Medical School Hamburg) multiprofessional experts collaborate on various research projects with a focus on bio-psycho-social health and education. Initially, the main goal was monitoring and evaluating the implementation of the International Classification of Functioning, Disability and Health (ICF) in clinical practice. Over time and based on the initial findings, the research group started developing new approaches to support training and education of health professionals in the use of the ICF.

Sparse Quadratic Approximation for Graph Learning.

Learning graphs represented by M-matrices via an l-regularized Gaussian maximum-likelihood method is a popular approach, but also one that poses computational challenges for large scale datasets. Recently proposed methods cast this problem as a constrained optimization variant of precision matrix estimation. In this paper, we build on a state-of-the-art sparse precision matrix estimation method and introduce two algorithms that learn M-matrices, that can be subsequently used for the estimation of graph Laplacian matrices.

On cheap entropy-sparsified regression learning.

Regression learning is one of the long-standing problems in statistics, machine learning, and deep learning (DL). We show that writing this problem as a probabilistic expectation over (unknown) feature probabilities - thus increasing the number of unknown parameters and seemingly making the problem more complex-actually leads to its simplification, and allows incorporating the physical principle of entropy maximization. It helps decompose a very general setting of this learning problem (including discretization, feature selection, and learning multiple piece-wise linear regressions) into an iterative sequence of simple substeps, which are either analytically solvable or cheaply computable through an efficient second-order numerical solver with a sublinear cost scaling.

Multiway -spectral graph cuts on Grassmann manifolds.

Nonlinear reformulations of the spectral clustering method have gained a lot of recent attention due to their increased numerical benefits and their solid mathematical background. We present a novel direct multiway spectral clustering algorithm in the -norm, for . The problem of computing multiple eigenvectors of the graph -Laplacian, a nonlinear generalization of the standard graph Laplacian, is recasted as an unconstrained minimization problem on a Grassmann manifold.