A PyTorch implementation of Principal Component Analysis (PCA) as an Autoencoder. This repository provides a simple and elegant way to perform PCA using PyTorch while leveraging autoencoder architecture.

Principal Component Analysis (PCA) is a fundamental technique in dimensionality reduction and feature extraction. In this repository, we implement PCA using the PyTorch framework, while modeling it as an autoencoder. This approach allows for easy integration with other neural network components and can be used for various tasks, including data compression, feature extraction, and anomaly detection.