Publications

Only recent publications are listed here. Poster contributions at workshops are also listed, as well as Arxiv papers.

Please also see Stian Anfinsen’s Google Scholar profile or Robert Jenssen’s Google Scholar profile.

2017

The Time Series Cluster Kernel
K. Mikalsen, F. Bianchi, C. Soguero-Ruiz and R. Jenssen
IEEE International Workshop on Machine Learning for Signal Processing, September 25-28, Roppongi, Tokyo, Japan

Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data
K. Mikalsen, F. Bianchi, C. Soguero-Ruiz and R. Jenssen
Pattern Recognition (pending minor revisions).

Using anchors from free text in electronic health records to diagnose postoperative delirium
K. Ø. Mikalsen, C. Soguero-Ruiz, K. Jensen, K. Hindberg, M. Gran, A. Revhaug. R-O. Lindsetmo, S. O. Skrøvseth, F. Godtliebsen and R. Jenssen
Computer Methods and Programs in Biomedicine

An Overview and Comparative Analysis of Recurrent Neural Networks for Short Term Load Forecasting
F. M. Bianchi, E. Maiorino, M. Kampffmeyer, A. Rizzi and R. Jenssen

Multiplex Visibility Graph to Investigate Recurrent Neural Networks Dynamics
F. M. Bianchi, L. Livi, C. Alippi and R. Jenssen
Scientific Reports 7:44037

Training Echo State Networks with Regularization through Dimensionality Reduction
S. Løkse, F. M. Bianchi and R. Jenssen
Cognitive Computation 9(3), 369-378


Gaussian Process Sensitivity Analysis for Oceanic Chlorophyll Estimation
K. Blix, G. Camps-Valls and R. Jenssen
Journal of Selected Topics in Applied Earth Obervations and Remote Sensing 10(4), 1265-1277

Determination of the Edge of Criticality in Echo State Networks Through Fisher Information Maximization
L. Livi, F. M. Bianchi, C. Alippi
IEEE Transactions on Neural Networks and Learning Systems

Analysis of Free Text in Electronic Health Records for Identification of Cancer Patient Trajectories
K. Jensen, C. Soguero-Ruiz, K. Ø. Mikalsen, R.-O. Lindsetmo, I. Kouskoumvekaki, M. Girolami, S. O. Skrøvseth, K. M. Augestad
Scientific Reports 7: 46226

Optimized Kernel Entropy Components
E. Izquierdo-Verdiguier, V. Lappara, R. Jenssen, L. Gomez-Chova and G. Camps-Valls
IEEE Transactions on Neural Networks and Learning Systems 28(6), 1466-1472

Urban Land Cover Classification with Missing Data Using Deep Convolutional Neural Networks
M. Kampffmeyer, A.-B. Salberg and R. Jenssen
IGARSS 2017 (invited paper, special session on deep learning in remote sensing)

A Clustering Approach to Heterogeneous Change Detection
L. T. Luppino, S. N. Anfinsen, G. Moser, R. Jenssen, F. M. Bianchi, S. Serpico, G. Mercier
SCIA 2017

Spectral Clustering using PCKID – A Probabilistic Cluster Kernel for Incomplete Data
S. Løkse, F. M. Bianchi, A.-B. Salberg and R. Jenssen
SCIA 2017

Deep Kernelized Autoencoders [Best Student Paper]
M. Kampffmeyer, S. Løkse, F. M. Bianchi, R. Jenssen and L. Livi
SCIA 2017

Temporal Overdrive Recurrent Neural Network
F. M. Bianchi, M. Kampffmeyer, E. Maiorino and R. Jenssen
IJCNN 2017

Critical echo state network dynamics by means of Fisher information maximization
F. M. Bianchi, L. Livi, R. Jenssen and C. Alippi
IJCNN 2017

Density Ridge Manifold Traversal
J. Myhre, M. Kampffmeyer and R. Jenssen
ICASSP 2017

Local Short Term Electricity Load Forecasting: Automatic Approaches
Đ. H. T. Hiển, F. M. Bianchi and R. Olsson
IJCNN 2017

2016

  • E. Maiorino, F. M. Bianchi, L. Livi, A. Rizzi, A. Sadeghian, “Data-driven detrending of nonstationary fractal time series with echo state networks”, Information Sciences [link].
  • F. M. Bianchi, L. Livi, C. Alippi, “Investigating echo state networks dynamics by means of recurrence analysis”, IEEE Transactions on Neural Networks and Learning Systems [link].
  • J. Myhre, M. Shaker, D. Kaba, R. Jenssen and D. Erdogmus, “Non-Parametric Manifold Unwrapping using Kernel Density Ridges” [arxiv].
  • C. Soguero-Ruiz, K. Hindberg, I. Mora-Jimenez, J. L. Rojo-Alvarez, S. O. Skrøvseth, F. Godtliebsen, K. Mortensen, A. Revhaug, R.-O. Lindsetmo, K. M. Augestad and R. Jenssen, “Predicting Colorectal Surgical Complications using Heterogeneous Clinical Data and Kernel Methods,” Journal of Biomedical Informatics.
  • V. Vikjord and R. Jenssen, “Information Theoretic Clustering Using a k-Nearest Neighbors-based Divergence Measure,” Book Chapter in Handbook of Pattern Recognition and Computer Vision (5th ed., C. H. Chen, Editor).
  • M. Kampffmeyer, A.-B. Salberg and R. Jenssen, “Detection of small objects, land cover mapping and modelling of uncertainty in urban remote sensing images using deep convolutional neural network,” CVPR Workshop on Visual Analysis in Satellite to Street Imagery.
  • J. Myhre, M. Kampffmeyer and R. Jenssen, “Ambient Space Manifold Learning using Density Ridges,” ICML Workshop on Geometry in Machine Learning.
  • J. Myhre, R. Jenssen and D. Erdogmus, “Geometric Interpretation of Density Ridge Manifold Estimation,” ICML Workshop on Geometry in Machine Learning.
  • J. Myhre, K. Ø. Mikalsen, S. Løkse and R. Jenssen, “Robust Non-Parametric Mode Clustering,” NIPS Workshop on Adaptive and Scalable Nonparametric Methods in Machine Learning.
  • K. Ø. Mikalsen, F. M. Bianchi, C. Soguero-Ruiz, S. Skrøvseth, R.-O. Lindsetmo, A. Revhaug and R. Jenssen, “Learning Similarities between Irregularly Sampled Short Multivariate Time Series from EHRs,” ICPR Workshop in Pattern Recognition in Healthcare Analytics.

2015

  • E. Izquierdo-Verdiguier, R. Jenssen, L. Gomez-Chova and G. Camps-Valls, “Spectral Clustering with the Probabilistic Cluster Kernel,” Neurocomputing, 149, 1299-1304, doi:10.1016/j.neucom.2014.08.068.
  • M. Shaker, J. Myhre and D. Erdogmus, “Computationally Efficient Exact Calculation of Kernel Density Derivatives,” Journal of Signal Processing Systems, 81(3), 321-332, DOI 10.1007/s11265-014-0904-1.
  • C. Soguero-Ruiz, F. Wang, R. Jenssen, K. M. Augestad, J.-L. Rojo Alvarez, I. Mora Jimenez, R.-O. Lindsetmo, S. O. Skrøvseth, “Data-driven Temporal Prediction of Surgical Site Infection,” Annual Symposium on Medical Informatics (AMIA), San Francisco, USA.
  • C. Sogureo-Ruiz and R. Jenssen, “Kernel Covariance Series Smoothing,” IEEE MLSP, Boston, USA.
  • K. Blix, G. Camps-Valls and R. Jenssen, “Sensitivity Analysis of Gaussian Processes for Oceanic Chlorophyll Prediction,” IGARSS, Milan, Rome.
  • J. N. Myhre, K. Ø. Mikalsen. S. Løkse and R. Jenssen, “Consensus Clustering using kNN Mode Seeking,” Scandinavian Conference on Image Analysis, Copenhagen, Denmark.
  • K. Mikalsen, K. Hindberg, M. Gran, K. Jensen, C. Soguero Ruiz, A. Revhaug, R.-O. Lindsetmo, S. Skrøvseth, F. Godtliebsen and R. Jenssen, “Predicting Postoperative Delirium using Anchors,” NIPS workshop on machine learning in healthcare.
  • C. Soguero-Ruiz, K. Hindberg, I. Mora-Jimenez, J. L. Rojo-Alvarez, S. O. Skrøvseth, F. Godtliebsen, K. Mortensen, A. Revhaug, R.-O. Lindsetmo, K. M. Augestad and R. Jenssen, “Prediction of Surgical Complications using Heterogeneous Clinical Data and Kernel Methods,” NIPS workshop on machine learning in healthcare.

2014

  • C. Soguero-Ruiz, K. Hindberg, J. L. Rojo-Alvarez, S. O. Skrøvseth, F. Godtliebsen, K. Mortensen, A. Revhaug, R.-O. Lindsetmo, K. M. Augestad and R. Jenssen, “Support Vector Feature Selection for Early Detection of Anastomosis Leakage from Bag-of-Words in Electronic Health Records,” IEEE Journal of Biomedical and Health Informatics, 10.1109/JBHI.2014.2361688.
  • V. Vikjord and R. Jenssen, “Information Theoretic Clustering using a k-Nearest Neighbors Approach,” Pattern Recognition, 47(9), 3070-3081, doi:10.1016/j.patcog.2014.03.018.
  • A.-B. Salberg, S. Ø. Larsen, R. Jenssen, “Classification of Ocean Surface Slicks using Hybrid-Polarimetric SAR Data,” SPIE Remote Sensing.
  • M. Shaker, J. Myhre, D. Kaba and D. Erdogmus, “Invertible Nonlinear Cluster Unwrapping,” IEEE MLSP, Reims, France.
  • J. Agersborg and R. Jenssen, “Mean Shift Spectral Clustering using Kernel Entropy Component Analysis,” IEEE MLSP, Reims, France.
  • C. Soguero-Ruiz, K. Hindberg, J. L. Rojo-Alvarez, S. O. Skrovseth, F. Godtliebsen, K. Mortensen, A. Revhaug, R.-O. Lindsetmo, I. Mora-Jimenez, K. M. Augestad and R. Jenssen, “Bootstrap Resampling Feature Selection and Support Vector Machine for Early Detection of Anastomosis Leakage,” in Proc. IEEE-EMBS BHI, Valencia, Spain.
  • C. Soguero-Ruiz, K. Hindberg, I. Mora-Jimenez, J. L. Rojo-Alvarez, S. O. Skrovseth, F. Godtliebsen, K. Mortensen, A. Revhaug, R.-O. Lindsetmo, I. Mora-Jimenez, K. M. Augestad and R. Jenssen, “Feature selection using Kernel Component Analysis For Early Detection Of Anastomosis Leakage,” in Proc. Workshop on Pattern Recognition on Healthcare Analytics, ICPR, Stockholm, Sweden.

2013

  • R. Jenssen, “Entropy-Relevant Dimensions in Kernel Feature Space,” IEEE Signal Processing Magazine, 30(4), pp. 30-39.
  • R. Jenssen, “Mean Vector Component Analysis for Visualization and Clustering of Non-Negative Data”, IEEE Transactions on Neural Networks and Learning Systems, 24(10), pp. 1553-1564.
  • V. Vikjord and R. Jenssen, “A New Information Theoretic Clustering Algorithm using k-NN”, IEEE MLSP, Southampton, UK.
  • R. Jenssen, “Mean Vector Component Analysis: A New Approach to Un-Centered PCA for Non-Negative Data”, IEEE MLSP, Southampton, UK.

 

 

 

Comments are closed.