Authors:
Martin Becker
;
Jens Lippel
and
André Stuhlsatz
Affiliation:
University of Applied Sciences Düsseldorf, Germany
Keyword(s):
High-dimensional Data, Dimensionality Reduction, Data Visualization, Discriminant Analysis, GerDA, Deep Autoencoder, Deep Neural Networks, Regularization, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
Databases and Visualization, Visual Data Mining
;
General Data Visualization
;
High-Dimensional Data and Dimensionality Reduction
;
Information and Scientific Visualization
;
Large Data Visualization
;
Visualization Algorithms and Technologies
Abstract:
We present a novel approach to dimensionality reduction for data visualization that is a combination of two
deep neural networks (DNNs) with different objectives. One is a nonlinear generalization of Fisher’s linear
discriminant analysis (LDA). It seeks to improve the class separability in the desired feature space, which is
a natural strategy to obtain well-clustered visualizations. The other DNN is a deep autoencoder. Here, an
encoding and a decoding DNN are optimized simultaneously with respect to the decodability of the features
obtained by encoding the data. The idea behind the combined DNN is to use the generalized discriminant
analysis as an encoding DNN and to equip it with a regularizing decoding DNN. Regarding data visualization,
a well-regularized DNN guarantees to learn sufficiently similar data visualizations for different sets of samples
that represent the data approximately equally good. Clearly, such a robustness against fluctuations in the
data is essential for real-
world applications. We therefore designed two extensive experiments that involve
simulated fluctuations in the data. Our results show that the combined DNN is considerably more robust
than the generalized discriminant analysis alone. Moreover, we present reconstructions that reveal how the
visualizable features look like back in the original data space.
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