L1-Regularized Regression Task

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):
  • L1-Norm Regularized Logistic Regression.
  • …
• Counter-Example(s):
  • L2-Norm Regularization.
  • L0 Norm Regularization.
• See: L1-Norm Regularizer, Regularization.

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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.

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