Abstract: Here, we propose a structure-preserving deep autoencoder-based dimensionality reduction scheme for data visualization. For this, we introduce two regularizers for regularizing autoencoders.

This project implements an autoencoder-based approach for dimensionality reduction. The autoencoder is trained to compress and reconstruct high-dimensional data efficiently, enabling effective feature ...

This is called dimensionality reduction. The two most common techniques ... Understanding Neural Autoencoders The diagram in Figure 2 illustrates a neural autoencoder. The autoencoder has the same ...

I want to try a few new methods on this dataset, for example feature reduction with an Autoencoder. The main dataset contains 120 features and one binary target variable. Using the supplementary ...

This leads to fewer human interventions in feature selection and preprocessing, resulting in higher model robustness and accuracy. In this study, researchers designed an autoencoder neural network and ...

An autoencoder (AE) is a neural network model primarily used for unsupervised learning. It achieves dimensionality reduction and feature extraction by learning to encode input data. The basic ...