Manifold learning with iterative dimensionality photo-projection

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

In this work, we propose a new dimensionality reduction approach for generating low-dimensional embeddings of high-dimensional data based on an iterative procedure. The data set's dimensions are sorted depending on their variance. Starting with the highest variance, the dimensions are iteratively projected onto the embedding. The projection can be seen as taking a photo from a two-dimensional motive employing a depth effect. The approach is flexible and offers numerous extensions for future work. We introduce a basic variant and illustrate it working mechanisms with numerous visualizations. The approach is experimentally analyzed on a small set of benchmark problems. Exemplary embeddings and evaluations based on the Shepard-Kruskal measure and the co-ranking matrix complement the analysis. The new approach shows competitive results in comparison to well-established dimensionality reduction methods.

OriginalsprogEngelsk
Titel2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
Antal sider7
ForlagInstitute of Electrical and Electronics Engineers Inc.
Publikationsdato30 jun. 2017
Sider2555-2561
Artikelnummer7966167
ISBN (Elektronisk)9781509061815
DOI
StatusUdgivet - 30 jun. 2017
Eksternt udgivetJa
Begivenhed2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, USA
Varighed: 14 maj 201719 maj 2017

Konference

Konference2017 International Joint Conference on Neural Networks, IJCNN 2017
LandUSA
ByAnchorage
Periode14/05/201719/05/2017
SponsorBrain-Mind Institute (BMI), Budapest Semester in Cognitive Science (BSCS), Intel

ID: 223196256