21 citations · 77 across the 18 of their papers we have counts for
19 papers
Poisson Subspace Clustering: Focusing on the Essentials in Count Data
Collin Leiber, Kai Puolamäki, Heikki Mannila
Count data represented as a matrix of non-negative integer values, such as contingency tables, are prevalent across diverse domains. When clustering such data sets, specific method…
Gradient Boosting Mapping for Dimensionality Reduction and Feature Extraction
Anri Patron, Ayush Prasad, Hoang Phuc Hau Luu +1
A fundamental problem in supervised learning is to find a good set of features or distance measures. If the new set of features is of lower dimensionality and can be obtained by a…
Using Slisemap to interpret physical data
Lauri Seppäläinen, Anton Björklund, Vitus Besel +1
Manifold visualisation techniques are commonly used to visualise high-dimensional datasets in physical sciences. In this paper we apply a recently introduced manifold visualisation…
iplot: web-first visualisation platform for multidimensional data
Akihiro Tanaka, Juniper Tyree, Anton Björklund +2
iplot is an HTML5-based system for interactive exploration of data and machine learning models. A key aspect is interaction, not only for the interactive plots but also between…
SLISEMAP: Supervised dimensionality reduction through local explanations
Anton Björklund, Jarmo Mäkelä, Kai Puolamäki
Existing methods for explaining black box learning models often focus on building local explanations of model behaviour for a particular data item. It is possible to create global…
Estimating regression errors without ground truth values
Henri Tiittanen, Emilia Oikarinen, Andreas Henelius +1
Regression analysis is a standard supervised machine learning method used to model an outcome variable in terms of a set of predictor variables. In most real-world applications we…