# GT-PCA: Effective and Interpretable Dimensionality Reduction with General Transform-Invariant Principal Component Analysis

arXiv: https://arxiv.org/abs/2401.15623 · HTML: https://mlsanity.com/paper/2401.15623/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 28 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Deep Convolutional Inverse Graphics Network](https://mlsanity.com/paper/1503.03167/) (2015)
- [Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification](https://mlsanity.com/paper/2301.05869/) (2023)
- [Detecting deviations from second-order stationarity in locally stationary functional time series](https://mlsanity.com/paper/1808.04092/) (2018)
- [A Portmanteau-type test for detecting serial correlation in locally stationary functional time series](https://mlsanity.com/paper/2009.07312/) (2020)
- [Functional Time Series](https://mlsanity.com/paper/1502.07113/) (2015)

## Built on by (most cited first, 2 of 2)

- [Consumer-grade EEG-based Eye Tracking](https://mlsanity.com/paper/2503.14322/) (2025)
- [EEG-EyeTrack: A Benchmark for Time Series and Functional Data Analysis with Open Challenges and Baselines](https://mlsanity.com/paper/2504.03760/) (2025)

## Abstract (arXiv)

Data analysis often requires methods that are invariant with respect to specific transformations, such as rotations in case of images or shifts in case of images and time series. While principal component analysis (PCA) is a widely-used dimension reduction technique, it lacks robustness with respect to these transformations. Modern alternatives, such as autoencoders, can be invariant with respect to specific transformations but are generally not interpretable. We introduce General Transform-Invariant Principal Component Analysis (GT-PCA) as an effective and interpretable alternative to PCA and autoencoders. We propose a neural network that efficiently estimates the components and show that GT-PCA significantly outperforms alternative methods in experiments based on synthetic and real data.