activity
20142021
most citedScalable Probabilistic Matrix Factorization with Graph-Based Priors

27 citations · 46 across the 5 of their papers we have counts for

collaborators

6 papers

cs.GR2021★ 1 cited

Edge-Path Bundling: A Less Ambiguous Edge Bundling Approach

Markus Wallinger, Daniel Archambault, David Auber +2

Edge bundling techniques cluster edges with similar attributes (i.e. similarity in direction and proximity) together to reduce the visual clutter. All edge bundling techniques to d…

cs.LG2019★ 27 cited

Scalable Probabilistic Matrix Factorization with Graph-Based Priors

Jonathan Strahl, Jaakko Peltonen, Hiroshi Mamitsuka +1

In matrix factorization, available graph side-information may not be well suited for the matrix completion problem, having edges that disagree with the latent-feature relations lea…

cs.CG2019

Computing Stable Demers Cartograms

Soeren Nickel, Max Sondag, Wouter Meulemans +4

Cartograms are popular for visualizing numerical data for map regions. Maintaining correct adjacencies is a primary quality criterion for cartograms. When there are multiple data v…

cs.CG2016

Peacock Bundles: Bundle Coloring for Graphs with Globality-Locality Trade-off

Jaakko Peltonen, Ziyuan Lin

Bundling of graph edges (node-to-node connections) is a common technique to enhance visibility of overall trends in the edge structure of a large graph layout, and a large variety…

stat.ML2015

An Information Retrieval Approach to Finding Dependent Subspaces of Multiple Views

Ziyuan Lin, Jaakko Peltonen

Finding relationships between multiple views of data is essential both for exploratory analysis and as pre-processing for predictive tasks. A prominent approach is to apply variant…

q-bio.QM2014★ 18 cited

Toward computational cumulative biology by combining models of biological datasets

Ali Faisal, Jaakko Peltonen, Elisabeth Georgii +2

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We intro…