Prototypal Analysis and Prototypal Regression

By Chenyue Wu, Esteban G. Tabak. 2017

13 citations recorded by the source corpus.

Prototypal analysis is introduced to overcome two shortcomings of archetypal analysis: its sensitivity to outliers and its non-locality, which reduces its applicability as a learning tool. Same as archetypal analysis, prototypal analysis finds prototypes through convex combination of the data points and approximates the data through convex combination of the archetypes, but it adds a penalty for using prototypes distant from the data points for their reconstruction. Prototypal analysis can be extended---via kernel embedding---to probability distributions, since the convexity of the prototypes makes them interpretable as mixtures. Finally, prototypal regression is developed, a robust supervised procedure which allows the use of distributions as either features or labels.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning, Statistical Learning Theory, Signal Processing

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Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “Wu and Tabak [319] used a convex combination of training instances to represent a prototype, where the prototype does not necessarily need to be a member of the training set.”
This looks like that: deep learning for interpretable image recognition (2018, methodology): “Our work relates closely to other prototype classification techniques in machine learning [32, 2, 45], and very closely, to the Bayesian Case Model [18].”
Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains its Predictions (2017, methodology): “Our work is closely aligned with other prototype classification techniques in machine learning (Bien and Tibshirani 2011; Kim, Rudin, and Shah 2014; Priebe et al. 2003; Wu and Tabak 2017).”
This Looks Like That: Deep Learning for Interpretable Image Recognition (2018, unknown): “Our work also relates closely to other prototype classification techniques in machine learning [25, 3, 14, 5].”