640 citations · 796 across the 22 of their papers we have counts for
22 papers
Modeling Human Responses by Ordinal Archetypal Analysis
Anna Emilie J. Wedenborg, Michael Alexander Harborg, Andreas Bigom +6
This paper introduces a novel framework for Archetypal Analysis (AA) tailored to ordinal data, particularly from questionnaires. Unlike existing methods, the proposed method, Ordin…
Probabilistic Block Term Decomposition for the Modelling of Higher-Order Arrays
Jesper Løve Hinrich, Morten Mørup
Tensors are ubiquitous in science and engineering and tensor factorization approaches have become important tools for the characterization of higher order structure. Factorizations…
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted Networks
Nikolaos Nakis, Abdulkadir Çelikkanat, Morten Mørup
Graph representation learning (GRL) has become a prominent tool for furthering the understanding of complex networks providing tools for network embedding, link prediction, and nod…
Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model
Nikolaos Nakis, Abdulkadir Çelikkanat, Louis Boucherie +5
Graph representation learning has become a prominent tool for the characterization and understanding of the structure of networks in general and social networks in particular. Typi…
Piecewise-Velocity Model for Learning Continuous-time Dynamic Node Representations
Abdulkadir Çelikkanat, Nikolaos Nakis, Morten Mørup
Networks have become indispensable and ubiquitous structures in many fields to model the interactions among different entities, such as friendship in social networks or protein int…
HM-LDM: A Hybrid-Membership Latent Distance Model
Nikolaos Nakis, Abdulkadir Çelikkanat, Morten Mørup
A central aim of modeling complex networks is to accurately embed networks in order to detect structures and predict link and node properties. The latent space models (LSM) have be…
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations
Nikolaos Nakis, Abdulkadir Çelikkanat, Sune Lehmann Jørgensen +1
Graph Representation Learning (GRL) has become central for characterizing structures of complex networks and performing tasks such as link prediction, node classification, network…
Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data
Ali Mohebbi, Alexander R. Johansen, Nicklas Hansen +5
Continuous Glucose Monitoring (CGM) has enabled important opportunities for diabetes management. This study explores the use of CGM data as input for digital decision support tools…
Probabilistic PARAFAC2
Philip J. H. Jørgensen, Søren F. V. Nielsen, Jesper L. Hinrich +3
The PARAFAC2 is a multimodal factor analysis model suitable for analyzing multi-way data when one of the modes has incomparable observation units, for example because of difference…
Scalable Group Level Probabilistic Sparse Factor Analysis
Jesper L. Hinrich, Søren F. V. Nielsen, Nicolai A. B. Riis +6
Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation.…
Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data
Søren F. V. Nielsen, Kristoffer H. Madsen, Rasmus Røge +2
Dynamic functional connectivity (FC) has in recent years become a topic of interest in the neuroimaging community. Several models and methods exist for both functional magnetic res…
Bayesian Dropout
Tue Herlau, Morten Mørup, Mikkel N. Schmidt
Dropout has recently emerged as a powerful and simple method for training neural networks preventing co-adaptation by stochastically omitting neurons. Dropout is currently not grou…
Completely random measures for modelling block-structured networks
Tue Herlau, Mikkel N. Schmidt, Morten Mørup
Many statistical methods for network data parameterize the edge-probability by attributing latent traits to the vertices such as block structure and assume exchangeability in the s…
Adaptive Reconfiguration Moves for Dirichlet Mixtures
Tue Herlau, Morten Mørup, Yee Whye Teh +1
Bayesian mixture models are widely applied for unsupervised learning and exploratory data analysis. Markov chain Monte Carlo based on Gibbs sampling and split-merge moves are widel…
Non-parametric Bayesian modeling of complex networks
Mikkel N. Schmidt, Morten Mørup
Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observ…
Nonparametric Bayesian models of hierarchical structure in complex networks
Mikkel N. Schmidt, Tue Herlau, Morten Mørup
Analyzing and understanding the structure of complex relational data is important in many applications including analysis of the connectivity in the human brain. Such networks can…
The Infinite Degree Corrected Stochastic Block Model
Tue Herlau, Mikkel N. Schmidt, Morten Mørup
In Stochastic blockmodels, which are among the most prominent statistical models for cluster analysis of complex networks, clusters are defined as groups of nodes with statisticall…
Crowds, Bluetooth, and Rock-n-Roll. Understanding Music Festival Participant Behavior
Jakob Eg Larsen, Piotr Sapiezynski, Arkadiusz Stopczynski +2
In this paper we present a study of sensing and analyzing an offline social network of participants at a large-scale music festival (8 days, 130,000+ participants). We place 33 fix…
Infinite Multiple Membership Relational Modeling for Complex Networks
Morten Mørup, Mikkel N. Schmidt, Lars Kai Hansen
Learning latent structure in complex networks has become an important problem fueled by many types of networked data originating from practically all fields of science. In this pap…
Semi-Supervised Kernel PCA
Christian Walder, Ricardo Henao, Morten Mørup +1
We present three generalisations of Kernel Principal Components Analysis (KPCA) which incorporate knowledge of the class labels of a subset of the data points. The first, MV-KPCA,…
Scalable Tensor Factorizations for Incomplete Data
Evrim Acar, Tamara G. Kolda, Daniel M. Dunlavy +1
The problem of incomplete data - i.e., data with missing or unknown values - in multi-way arrays is ubiquitous in biomedical signal processing, network traffic analysis, bibliometr…
Multiplicative updates For Non-Negative Kernel SVM
Vamsi K. Potluru, Sergey M. Plis, Morten Morup +2
We present multiplicative updates for solving hard and soft margin support vector machines (SVM) with non-negative kernels. They follow as a natural extension of the updates for no…