Manifold learning with iterative dimensionality photo-projection
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Manifold learning with iterative dimensionality photo-projection. / Luckehe, Daniel; Oehmcke, Stefan; Kramer, Oliver.
2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 2555-2561 7966167.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Manifold learning with iterative dimensionality photo-projection
AU - Luckehe, Daniel
AU - Oehmcke, Stefan
AU - Kramer, Oliver
PY - 2017/6/30
Y1 - 2017/6/30
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85030868252&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2017.7966167
DO - 10.1109/IJCNN.2017.7966167
M3 - Article in proceedings
AN - SCOPUS:85030868252
SP - 2555
EP - 2561
BT - 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Joint Conference on Neural Networks, IJCNN 2017
Y2 - 14 May 2017 through 19 May 2017
ER -
ID: 223196256