applicable models
unsupervised
Works with unlabeled data
5 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Factor Analysis | Algorithmic | Architecture/model Agnostic Paradigm/unsupervised +1 | Tabular | Factor analysis is a statistical technique that identifies latent variables (hidden factors) underlying observed... | ||
| Principal Component Analysis | Algorithmic | Architecture/model Agnostic Paradigm/unsupervised +1 | Any | Principal Component Analysis transforms high-dimensional data into a lower-dimensional representation by finding the... | ||
| Prototype and Criticism Models | Algorithmic | Architecture/model Agnostic Paradigm/supervised +3 | Any | Prototype and Criticism Models provide data understanding by identifying two complementary sets of examples: prototypes... | ||
| Synthetic Data Generation | Algorithmic | Architecture/neural Networks/generative/gan Architecture/neural Networks/generative/vae +5 | Any | Synthetic data generation creates artificial datasets that aim to preserve the statistical properties, distributions,... | ||
| Fairness GAN | Algorithmic | Architecture/neural Networks/generative/gan Paradigm/generative +4 | Any | A data generation technique that employs Generative Adversarial Networks (GANs) to create fair synthetic datasets by... |
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