Aug 11, 2022 · This article proposed a novel autoencoder method using Mahalanobis distance metric of rescaling transformation to extract linearly separable ...
The key idea of the method is that by implementing Mahalanobis distance metric of rescaling transformation, the difference between the ...
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 ...
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To extract feature from the data in a high-dimensional space, this paper proposed a novel autoencoder approach based on Mahalanobis distance metric of rescaling ...
This paper proposes a robust autoencoder with Wasserstein distance metric to extract the linear separability features from the input data.
Jan 1, 2023 · This paper proposes a robust autoencoder with Wasserstein distance metric to extract the linear separability features from the input data.
Aug 11, 2022 · A novel autoencoder approach to feature extraction with linear separability for high-dimensional data Read the full article ...
Feb 16, 2023 · behind it is to project the linearly inseparable data onto a higher dimensional space where it becomes linearly separable”. “Unfortunately ...
Nov 12, 2023 · Convolutional autoencoder (CAE) feature extraction is shown to be better than autoencoder (AE) feature extraction in multiple studies.
Feb 3, 2024 · Another effective method to extract complex, hierarchical, and high-level features from nonlinear data is deep learning. Deep learning models ...
Missing: separability | Show results with:separability