Multi-Task Feature Learning Via Efficient l2, 1-Norm Minimization
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Summary
This paper proposes to accelerate the computation of the l2, 1-norm regularized regression model by reformulating it as two equivalent smooth convex optimization problems which are then solved via the Nesterov's method---an optimal first-order black-box method for smooth conveX optimization.
- Type
- preprint
- Published
- 2009-06-18
- Cited by
- 757
- References
- 36
- Access
- Open access
- OpenAlex
- https://openalex.org/W1871180460
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:51985230
Keywords
Norm (philosophy), Minification, Multi-task learning, Task (project management), Computer science
References
- EFFICIENT METHODS IN CONVEX PROGRAMMING
- Probabilistic Joint Feature Selection for Multi-task Learning
- Convex Optimization & Euclidean Distance Geometry
- Problem Complexity and Method Efficiency in Optimization
- Pattern Recognition and Machine Learning
- The Group-Lasso for generalized linear models: uniqueness of solutions and efficient algorithms
- A coordinate gradient descent method for nonsmooth separable minimization
- Convex multi-task feature learning
- Flexible latent variable models for multi-task learning
- A multivariate regression approach to association analysis of a quantitative trait network
- The group lasso for logistic regression
- Efficient Euclidean projections in linear time
- Large-scale sparse logistic regression
- Introductory Lectures on Convex Optimization - A Basic Course
- Regression Shrinkage and Selection via the Lasso
- Model selection and estimation in regression with grouped variables
- Regularized multi--task learning
- High-dimensional support union recovery in multivariate regression
- A Projected Subgradient Method for Scalable Multi-Task Learning
- Boosted Lasso
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- Feature-based transfer learning with real-world applications
- Sparse coding for machine learning, image processing and computer vision
- Inter-modality Relationship Constrained Multi-Task Feature Selection for AD/MCI Classification
- Probabilistic Multi-Label Classification with Sparse Feature Learning
- Multi-modality Canonical Feature Selection for Alzheimer’s Disease Diagnosis
- Online Learning for Group Lasso
- Learning with sparsity: Structures, optimization and applications
- Encoding Tree Sparsity in Multi-Task Learning: A Probabilistic Framework
- Anomaly Detection for Astronomical Data
- Embedded Unsupervised Feature Selection
- Human action recognition via multi-task learning base on spatial-temporal feature
- Multi-local-task learning with global regularization for object tracking
- Structured Sparsity with Group-Graph Regularization
- Non-convex feature learning via l p, ∞ operator
- Discriminative multi-view feature selection and fusion
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