A novel autoencoder approach to feature extraction with linear separability for high-dimensional data.

ABSTRACT: Feature extraction often needs to rely on sufficient information of the input data, however, the distribution of the data upon a high-dimensional space is too sparse to provide sufficient information for feature extraction. Furthermore, high dimensionality of the data also creates trouble for the searching of those features scattered in subspaces. As such, it is a tricky task for feature extraction from the data upon a high-dimensional space. To address this issue, this article proposes a novel autoencoder method using Mahalanobis distance metric of rescaling transformation. The key idea of the method is that by implementing Mahalanobis distance metric of rescaling transformation, the difference between the reconstructed distribution and the original distribution can be reduced, so as to i

SUBMITTER: Zheng J 

PROVIDER: S-EPMC10403198 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature