GT-PCA: Effective and Interpretable Dimensionality Reduction with General Transform-Invariant Principal Component Analysis Article Swipe
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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2401.15623
- https://arxiv.org/pdf/2401.15623
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391376259
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391376259Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2401.15623Digital Object Identifier
- Title
-
GT-PCA: Effective and Interpretable Dimensionality Reduction with General Transform-Invariant Principal Component AnalysisWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-28Full publication date if available
- Authors
-
Florian HeinrichsList of authors in order
- Landing page
-
https://arxiv.org/abs/2401.15623Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2401.15623Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2401.15623Direct OA link when available
- Concepts
-
Principal component analysis, Dimensionality reduction, Invariant (physics), Pattern recognition (psychology), Mathematics, Curse of dimensionality, Sparse PCA, Artificial intelligence, Computer science, Statistics, Mathematical physicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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