Now showing items 1-10 of 60
A Scatter-Based Prototype Framework and Multi-Class Extension of Support Vector Machines
(Journal article; Tidsskriftartikkel; Peer reviewed, 2012)
We provide a novel interpretation of the dual of support vector machines (SVMs) in terms of scatter with respect to class prototypes and their mean. As a key contribution, we extend this framework to multiple classes, providing a new joint Scatter SVM algorithm, at the level of its binary counterpart in the number of optimization variables. This enables us to implement computationally efficient ...
Using anchors from free text in electronic health records to diagnose postoperative delirium
(Journal article; Tidsskriftartikkel; Peer reviewed, 2017-09-19)
Objectives:<br> Postoperative delirium is a common complication after major surgery among the elderly. Despite its potentially serious consequences, the complication often goes undetected and undiagnosed. In order to provide diagnosis support one could potentially exploit the information hidden in free text documents from electronic health records usin ...
The deep kernelized autoencoder
(Journal article; Tidsskriftartikkel; Peer reviewed, 2018-07-18)
Autoencoders learn data representations (codes) in such a way that the input is reproduced at the output of the network. However, it is not always clear what kind of properties of the input data need to be captured by the codes. Kernel machines have experienced great success by operating via inner-products in a theoretically well-defined reproducing kernel Hilbert space, hence capturing topological ...
Multiplex visibility graphs to investigate recurrent neural network dynamics
(Journal article; Tidsskriftartikkel; Peer reviewed, 2017-03-10)
A recurrent neural network (RNN) is a universal approximator of dynamical systems, whose performance often depends on sensitive hyperparameters. Tuning them properly may be difficult and, typically, based on a trial-and-error approach. In this work, we adopt a graph-based framework to interpret and characterize internal dynamics of a class of RNNs called echo state networks (ESNs). We design principled ...
"Intelligente" læringssystemer: Fra leken Furby til spamfiltre til miljø
(Conference object; Konferansebidrag, 2010-09-02)
Joint optimization of an autoencoder for clustering and embedding
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-06-21)
Deep embedded clustering has become a dominating approach to unsupervised categorization of objects with deep neural networks. The optimization of the most popular methods alternates between the training of a deep autoencoder and a k-means clustering of the autoencoder’s embedding. The diachronic setting, however, prevents the former to beneft from valuable information acquired by the latter. In ...
Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning
(Journal article; Tidsskriftartikkel; Peer reviewed, 2018-01-09)
To maintain the reliability, availability, and sustainability of electricity supply, electricity companies regularly perform visual inspections on their transmission and distribution networks. These inspections have been typically carried out using foot patrol and/or helicopter-assisted methods to plan for necessary repair or replacement works before any major damage, which may cause power outage. ...
Consensus Clustering Using kNN Mode Seeking
(Chapter; Bokkapittel, 2015-06-09)
In this paper we present a novel clustering approach which combines two modern strategies, namely consensus clustering, and two stage clustering as represented by the mean shift spectral clustering algorithm. We introduce the recent kNN mode seeking algorithm in the consensus clustering framework, and the information theoretic kNN Cauchy Schwarz divergence as foundation for spectral clustering. In ...
Gaussian Process Sensitivity Analysis for Oceanic Chlorophyll Estimation
(Journal article; Tidsskriftartikkel; Peer reviewed, 2017-01-04)
Gaussian process regression (GPR) has experienced tremendous success in biophysical parameter retrieval in the past years. The GPR provides a full posterior predictive distribution so one can derive mean and variance predictive estimates, i.e., point-wise predictions and associated confidence intervals. GPR typically uses translation invariant covariances that make the prediction function very ...
Deep divergence-based approach to clustering
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-02-08)
A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminative loss function. As opposed to supervised deep learning, this line of research is in its infancy, and how to design and optimize suitable loss functions to train deep neural networks for clustering is still an open question. ...