L1-Regularized Regression Task
(Redirected from L1 Regression)
Jump to navigation
Jump to search
An L1-Regularized Regression Task is an regularized regression task that optimizes an L1-norm.
- AKA: ℓ1 Regularization.
- Context:
- It can be solved by an L1-Regularized Optimization System (that implements an l1-regularized optimization algorithm)
- Example(s):
- Counter-Example(s):
- See: L1-Norm Regularizer, Regularization.
References
2017
- https://towardsdatascience.com/l1-and-l2-regularization-methods-ce25e7fc831c
- QUOTE: ... A regression model that uses L1 regularization technique is called Lasso Regression and model which uses L2 is called Ridge Regression. The key difference between these two is the penalty term. …
2012
- (Mohamed et al., 2012) ⇒ Shakir Mohamed, Katherine Heller, and Zoubin Ghahramani. (2012). “Bayesian and L1 Approaches to Sparse Unsupervised Learning.” In: Proceedings of the 29th International Conference on Machine Learning (ICML-12).
- QUOTE: The use of L1 regularisation for sparse learning has generated immense research interest, with successful application in such diverse areas as signal acquisition, image coding, genomics and collaborative filtering.