no code implementations • 21 Feb 2024 • Martin Ryner, Jan Kronqvist, Johan Karlsson
Clustering is one of the most fundamental tools in data science and machine learning, and k-means clustering is one of the most common such methods.
no code implementations • 25 Jul 2022 • Martin Ryner, Johan Karlsson
In this paper we propose an adaptive approach for clustering and visualization of data by an orthogonalization process.
no code implementations • 6 Oct 2021 • Filip Elvander, Johan Karlsson, Toon van Waterschoot
In this work, we consider the problem of bounding the values of a covariance function corresponding to a continuous-time stationary stochastic process or signal.
no code implementations • 1 Oct 2021 • Jiaojiao Fan, Isabel Haasler, Johan Karlsson, Yongxin Chen
Multi-marginal optimal transport (MOT) is a generalization of optimal transport to multiple marginals.
no code implementations • 28 Jun 2021 • Filip Elvander, Johan Karlsson
Furthermore, we consider approximating signals with arbitrary spectral densities by sequences of singular spectrum, i. e., sinusoidal, processes, and derive the limiting behavior of covariance estimates as both the sample size and the number of sinusoidal components tend to infinity.
no code implementations • 15 Jun 2021 • Johan Edstedt, Amanda Berg, Michael Felsberg, Johan Karlsson, Francisca Benavente, Anette Novak, Gustav Grund Pihlgren
Automatically identifying harmful content in video is an important task with a wide range of applications.
no code implementations • 26 Jun 2020 • Rahul Singh, Isabel Haasler, Qinsheng Zhang, Johan Karlsson, Yongxin Chen
We consider incremental inference problems from aggregate data for collective dynamics.
3 code implementations • 25 Jun 2020 • Isabel Haasler, Rahul Singh, Qinsheng Zhang, Johan Karlsson, Yongxin Chen
We study multi-marginal optimal transport problems from a probabilistic graphical model perspective.
no code implementations • 31 Mar 2020 • Rahul Singh, Isabel Haasler, Qinsheng Zhang, Johan Karlsson, Yongxin Chen
Consequently, the celebrated Sinkhorn/iterative scaling algorithm for multi-marginal optimal transport can be leveraged together with the standard belief propagation algorithm to establish an efficient inference scheme which we call Sinkhorn belief propagation (SBP).
no code implementations • 6 Apr 2019 • Ali Sadeghian, Deoksu Lim, Johan Karlsson, Jian Li
The use of distances based on optimal transportation has recently shown promise for discrimination of power spectra.
1 code implementation • 2 Aug 2018 • Sebastian Banert, Axel Ringh, Jonas Adler, Johan Karlsson, Ozan Öktem
In this work, we consider methods for solving large-scale optimization problems with a possibly nonsmooth objective function.
Optimization and Control 90C25 (Primary) 68T05, 47H05 (Secondary)
1 code implementation • 30 Oct 2017 • Jonas Adler, Axel Ringh, Ozan Öktem, Johan Karlsson
We propose using the Wasserstein loss for training in inverse problems.
2 code implementations • 7 Dec 2016 • Johan Karlsson, Axel Ringh
In particular we consider a limited-angle computerized tomography problem, where a priori information is used to compensate for missing measurements.
Optimization and Control 49N45, 90C25, 94A08