TY - GEN
T1 - User-guided Dimensionality Reduction Ensembles.
AU - Hilasaca, Gladys M. H.
AU - Paulovich, Fernando Vieira
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2019/7
Y1 - 2019/7
N2 - Dimensionality Reduction (DR) techniques are widely used to analyze and make sense of high-dimensional data. Each method is geared towards preserving a different aspect of the data. For example, some techniques favor neighborhood preservation whereas others favor distance preservation. While these DR techniques help users to represent their data, it makes a complex task to select a suitable DR. Also, most DR techniques have additional parameters that affect the results, which make the task of choosing a technique more difficult. Existing methods compare DR techniques using some quality metrics, and some of them combine DR outputs by averaging projections. However, it does not yet provide enough mechanisms to create a new DR according to user requirements. In this paper, we present a way to analyze and compare different DR techniques. It is an interactive assessment method that allows a user to explore known DR techniques, identify the differences between them, and create a new DR technique that combines existing techniques to match user expectations.
AB - Dimensionality Reduction (DR) techniques are widely used to analyze and make sense of high-dimensional data. Each method is geared towards preserving a different aspect of the data. For example, some techniques favor neighborhood preservation whereas others favor distance preservation. While these DR techniques help users to represent their data, it makes a complex task to select a suitable DR. Also, most DR techniques have additional parameters that affect the results, which make the task of choosing a technique more difficult. Existing methods compare DR techniques using some quality metrics, and some of them combine DR outputs by averaging projections. However, it does not yet provide enough mechanisms to create a new DR according to user requirements. In this paper, we present a way to analyze and compare different DR techniques. It is an interactive assessment method that allows a user to explore known DR techniques, identify the differences between them, and create a new DR technique that combines existing techniques to match user expectations.
KW - Dimensionality reduction
KW - ensemble learning
KW - quality metrics
KW - regression model
KW - user interaction
UR - http://www.scopus.com/inward/record.url?scp=85072303999&partnerID=8YFLogxK
U2 - 10.1109/IV.2019.00046
DO - 10.1109/IV.2019.00046
M3 - Conference contribution
SP - 228
EP - 233
BT - Information Visualization - Biomedical Visualization and Geometric Modelling and Imaging, IV 2019
A2 - Banissi, Ebad
A2 - Ursyn, Anna
A2 - McK. Bannatyne, Mark W.
A2 - Datia, Nuno
A2 - Pires, Joao Moura
A2 - Francese, Rita
A2 - Sarfraz, Muhammad
A2 - Wyeld, Theodor G
A2 - Bouali, Fatma
A2 - Venturin, Gilles
A2 - Azzag, Hanane
A2 - Lebbah, Mustapha
A2 - Trutschl, Marjan
A2 - Cvek, Urska
A2 - Muller, Heimo
A2 - Nakayama, Minoru
A2 - Kernbach, Sebastian
A2 - Caruccio, Loredana
A2 - Risi, Michele
A2 - Erra, Ugo
A2 - Vitiello, Autilia
A2 - Rossano, Veronica
ER -