AugmentedPCA Documentation

Welcome to the AugmentedPCA docmentation!

AugmentedPCA is a Python package that provides implementations of linear factor models that find a set of factors aligned with an augmenting objective in addition to the canonical PCA objective of finding factors that represent the data variance.

Below is a list of content offered by this documentation. Included are installation instructions, an introduction to the package, models provided, and examples of how to use different provided models.

Contents

• Installation
  • Dependencies
  • Installation Instructions
• Introduction
  • Models Overview
  • Quick Introduction
• Models
  • Supervised AugmentedPCA
  • Adversarial AugmentedPCA
  • Approximate Inference Strategies
• Examples
  • SAPCA Example - Gene Expression Clustering
  • AAPCA Example - Removal of Image Nuisance
• Citation
  • Citation
  • Funding

Package Reference

• apca.models
  • Supervised AugmentedPCA
  • Adversarial AugmentedPCA
• apca.metrics
  • Reconstruction Error

Indices and Tables

• Index

• Module Index

• Search Page