Author:
Hirokazu Shimauchi
Affiliation:
Faculty of Engineering, Hachinohe Institute of Technology, 88-1 Obiraki Myo, Hachinohe-Shi, Hachinohe, Japan
Keyword(s):
Unsupervised Representation Learning, Quaiconformal Extension, Quaiconformal Mapping.
Abstract:
In this paper, we introduce a novel unsupervised representation learning method based on quasiconformal extension. It is essential to develop feature representations that significantly improve predictive performance, regardless of whether the approach is implicit or explicit. Quasiconformal extension extends a mapping to a higher dimension with a certain regularity. The method introduced in this study constructs a piecewise linear mapping of real line by leveraging the correspondence between the distribution of individual features and a uniform distribution. Subsequently, a higher-order feature representation is generated through quasiconformal extension, aiming to achieve effective representations. In experiments conducted across ten distinct datasets, our approach enhanced the performance of neural networks, extremely randomized trees, and support vector machines, when the features contained a sufficient level of information necessary for classification.