An online algorithm for contrastive Principal Component Analysis

TL;DR

This paper introduces an online, more interpretable version of contrastive PCA, called cPCA*, with a neural network implementation suitable for neuromorphic hardware, improving efficiency and hyper-parameter sensitivity.

Contribution

The authors develop an online algorithm for cPCA*, enhancing interpretability and robustness, and demonstrate its neural network mapping for energy-efficient hardware implementation.

Findings

cPCA* is less sensitive to hyper-parameter choices.

The online algorithm performs well on real datasets.

It maps onto a neural network with local learning rules.

Abstract

Finding informative low-dimensional representations that can be computed efficiently in large datasets is an important problem in data analysis. Recently, contrastive Principal Component Analysis (cPCA) was proposed as a more informative generalization of PCA that takes advantage of contrastive learning. However, the performance of cPCA is sensitive to hyper-parameter choice and there is currently no online algorithm for implementing cPCA. Here, we introduce a modified cPCA method, which we denote cPCA*, that is more interpretable and less sensitive to the choice of hyper-parameter. We derive an online algorithm for cPCA* and show that it maps onto a neural network with local learning rules, so it can potentially be implemented in energy efficient neuromorphic hardware. We evaluate the performance of our online algorithm on real datasets and highlight the differences and similarities with the original formulation.