ArticleACM transactions on knowledge discovery from data2012
Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks.
Article in ACM transactions on knowledge discovery from data, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2023Article
- Efficient multi-task learning with adaptive temporal structure for progression prediction.Neural computing & applications · 2023Article
- The Identification of ECG Signals Using WT-UKF and IPSO-SVM.Sensors (Basel, Switzerland) · 2022Article
- Model-Protected Multi-Task Learning.IEEE transactions on pattern analysis and machine intelligence · 2022Article
- Quantifying risk factors in medical reports with a context-aware linear model.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Classification of multi-site MR images in the presence of heterogeneity using multi-task learning.NeuroImage. Clinical · 2018Article
- Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.Bioinformatics (Oxford, England) · 2016Article
- AcconPred: Predicting Solvent Accessibility and Contact Number Simultaneously by a Multitask Learning Framework under the Conditional Neural Fields Model.BioMed research international · 2015Article
- Multi-Stage Multi-Task Feature Learning.Advances in neural information processing systems · 2013Article
- A convex formulation for learning a shared predictive structure from multiple tasks.IEEE transactions on pattern analysis and machine intelligence · 2013Article
- Article
- Robust Multi-Task Feature Learning.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2012Article
- Optimal Exact Least Squares Rank Minimization.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2012Article
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Authors and funding
3 authors.
Funding
Abstract
We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the sparse and low-rank patterns are induced by a cardinality regularization term and a low-rank constraint, respectively. This formulation is non-convex; we convert it into its convex surrogate, which can be routinely solved via semidefinite programming for small-size problems. We propose to employ the general projected gradient scheme to efficiently solve such a convex surrogate; however, in the optimization formulation, the objective function is non-differentiable and the feasible domain is non-trivial. We present the procedures for computing the projected gradient and ensuring the global convergence of the projected gradient scheme. The computation of projected gradient involves a constrained optimization problem; we show that the optimal solution to such a problem can be obtained via solving an unconstrained optimization subproblem and an Euclidean projection subproblem. We also present two projected gradient algorithms and analyze their rates of convergence in details. In addition, we illustrate the use of the presented projected gradient algorithms for the proposed multi-task learning formulation using the least squares loss. Experimental results on a collection of real-world data sets demonstrate the effectiveness of the proposed multi-task learning formulation and the efficiency of the proposed projected gradient algorithms.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.