A tied-weight autoencoder for the linear dimensionality reduction of sample data.

ABSTRACT: Dimensionality reduction is a method used in machine learning and data science to reduce the dimensions in a dataset. While linear methods are generally less effective at dimensionality reduction than nonlinear methods, they can provide a linear relationship between the original data and the dimensionality-reduced representation, leading to better interpretability. In this research, we present a tied-weight autoencoder as a dimensionality reduction model with the merit of both linear and nonlinear methods. Although the tied-weight autoencoder is a nonlinear dimensionality reduction model, we approximate it to function as a linear model. This is achieved by removing the hidden layer units that are largely inactivated by the input data, while preserving the model's effectiveness. We evaluate

SUBMITTER: Kim S 

PROVIDER: S-EPMC11538474 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature