Non-Linear Feature Transformation
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A Non-Linear Feature Transformation is a feature transformation that involves cross-products.
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- Counter-Example(s):
- See: Cross-Product Feature Transformation.
References
2016
- (Cheng et al., 2016) ⇒ Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. (2016). “Wide & Deep Learning for Recommender Systems.” In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ISBN:978-1-4503-4795-2 doi:10.1145/2988450.2988454
- QUOTE: Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort.