ℓ1 Norm Minimization Task: Difference between revisions
Jump to navigation
Jump to search
m (Text replacement - "]." In:" to "].” In:") |
m (Remove links to pages that are actually redirects to this page.) |
||
| Line 10: | Line 10: | ||
=== 2013 === | === 2013 === | ||
* ([[2013_FastMinimizationAlgorithmsforRo|Yang et al., 2013]]) ⇒ [[Allen Y Yang]], [[Zihan Zhou]], [[Arvind Ganesh Balasubramanian]], [[S Shankar Sastry]], and [[Yi Ma]]. ([[2013]]). “[http://arxiv.org/pdf/1007.3753 Fast-minimization Algorithms for Robust Face Recognition].” In: Image Processing, IEEE Transactions on, 22(8). | * ([[2013_FastMinimizationAlgorithmsforRo|Yang et al., 2013]]) ⇒ [[Allen Y Yang]], [[Zihan Zhou]], [[Arvind Ganesh Balasubramanian]], [[S Shankar Sastry]], and [[Yi Ma]]. ([[2013]]). “[http://arxiv.org/pdf/1007.3753 Fast-minimization Algorithms for Robust Face Recognition].” In: Image Processing, IEEE Transactions on, 22(8). | ||
** QUOTE: [[ | ** QUOTE: [[ℓ1 Norm Minimization Task|<i>l</i>1-minimization]] refers to [[finding the minimum]] [[l1-norm solution]] to an [[underdetermined linear system]] </math>b=Ax</math>. </s> Under certain conditions as described in [[compressive sensing theory]], the [[minimum solution|minimum]] [[l1-norm solution]] is also the [[sparsest solution]]. </s> | ||
---- | ---- | ||
__NOTOC__ | __NOTOC__ | ||
[[Category:Concept]] | [[Category:Concept]] | ||
Revision as of 20:45, 23 December 2019
An ℓ1 Norm Minimization Task is a minimization task that is required to find an l1-norm solution to an underdetermined linear system </math>b=Ax</math>.
- Context:
- It can be solved by an ℓ1 Minimization System (that implements an ℓ1 Minimization Algorithm).
- It can range from being a Constrained ℓ1 Minimization Task to being an Unconstrained ℓ1 Minimization Task.
- See: Covariance Matrix, ℓ2 Minimization, ℓ1 Norm.
References
2013
- (Yang et al., 2013) ⇒ Allen Y Yang, Zihan Zhou, Arvind Ganesh Balasubramanian, S Shankar Sastry, and Yi Ma. (2013). “Fast-minimization Algorithms for Robust Face Recognition.” In: Image Processing, IEEE Transactions on, 22(8).
- QUOTE: l1-minimization refers to finding the minimum l1-norm solution to an underdetermined linear system </math>b=Ax</math>. Under certain conditions as described in compressive sensing theory, the minimum l1-norm solution is also the sparsest solution.