Supervised Quadratic Feature Analysis: Information Geometry Approach for Dimensionality Reduction
arXiv:2502.00168v5 Announce Type: replace-cross Abstract: Supervised dimensionality reduction maps labeled data into a low-dimensional feature space while preserving class separation. A common strategy is to learn features that maximize a measure of statistical dissimilarity between the class-conditional probability distributions. Information geometry, which is rooted in Riemannian geometry, provides an alternative framework for measuring class dissimilarity. It treats probability distributions as points in a statistical manifold and uses the Fisher information metric to define a geodesic dist
This research is published as AI systems become increasingly complex, demanding more efficient and theoretically sound approaches to data dimensionality reduction and understanding.
Sophisticated dimensionality reduction techniques like Supervised Quadratic Feature Analysis, underpinned by information geometry, are crucial for advancing AI model efficiency, interpretability, and performance in critical applications.
The theoretical foundation for supervised dimensionality reduction is deepened by incorporating Riemannian geometry, offering a more robust framework for preserving class separation and improving feature learning.
- · AI researchers
- · Machine learning engineers
- · Data scientists
- · Deep learning frameworks
- · Inefficient AI models
- · Brute-force feature engineering methods
Improved performance and reduced computational cost in AI models due to more effective feature selection.
Acceleration of research into more geometrically informed AI architectures and learning paradigms.
Enhanced AI capabilities across various sectors, from medical diagnostics to autonomous systems, benefiting from more precise data representations.
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Read at arXiv cs.LG