P
Patrik O. Hoyer
Researcher at University of Helsinki
Publications - 78
Citations - 11543
Patrik O. Hoyer is an academic researcher from University of Helsinki. The author has contributed to research in topics: Causal model & Neural coding. The author has an hindex of 37, co-authored 78 publications receiving 10422 citations. Previous affiliations of Patrik O. Hoyer include Helsinki Institute for Information Technology & Helsinki University of Technology.
Papers
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Journal ArticleDOI
Non-negative Matrix Factorization with Sparseness Constraints
TL;DR: In this paper, the notion of sparseness is incorporated into NMF to improve the found decompositions, and the authors provide complete MATLAB code both for standard NMF and for their extension.
Journal ArticleDOI
A Linear Non-Gaussian Acyclic Model for Causal Discovery
TL;DR: This work shows how to discover the complete causal structure of continuous-valued data, under the assumptions that (a) the data generating process is linear, (b) there are no unobserved confounders, and (c) disturbance variables have non-Gaussian distributions of non-zero variances.
Proceedings ArticleDOI
Non-negative sparse coding
TL;DR: A simple yet efficient multiplicative algorithm for finding the optimal values of the hidden components of non-negative sparse coding and how the basis vectors can be learned from the observed data is shown.
Proceedings Article
Nonlinear causal discovery with additive noise models
TL;DR: It is shown that the basic linear framework can be generalized to nonlinear models and, in this extended framework, nonlinearities in the data-generating process are in fact a blessing rather than a curse, as they typically provide information on the underlying causal system and allow more aspects of the true data-Generating mechanisms to be identified.
Book
Natural Image Statistics
TL;DR: Natural Image Statistics as discussed by the authors is a comprehensive introduction to the multidisciplinary field of natural image statistics, which can be used in any discipline related to vision, such as neuroscience, computer science, psychology, electrical engineering, cognitive science or statistics.