ArticleAdvances in neural information processing systems2013
Multi-Stage Multi-Task Feature Learning.
Article in Advances in neural information processing systems, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks.Proceedings of machine learning research · 2024Article
- Boosting Tree-Assisted Multitask Deep Learning for Small Scientific Datasets.Journal of chemical information and modeling · 2020Article
- Sparse discriminative latent characteristics for predicting cancer drug sensitivity from genomic features.PLoS computational biology · 2019Article
- Enter the Matrix: Factorization Uncovers Knowledge from Omics.Trends in genetics : TIG · 2018Review
- Longitudinal Genotype-Phenotype Association Study through Temporal Structure Auto-Learning Predictive Model.Journal of computational biology : a journal of computational molecular cell biology · 2018Article
- Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms.Physics in medicine and biology · 2017Article
- TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions.PLoS computational biology · 2017Article
- Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-Learning Predictive Model.Research in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005- ) · 2017Article
- Neural oscillations as a signature of efficient coding in the presence of synaptic delays.eLife · 2016Article
- Multiplicative Multitask Feature Learning.Journal of machine learning research : JMLR · 2016Article
- Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder.Brain imaging and behavior · 2016Article
- A Learning-Based CT Prostate Segmentation Method via Joint Transductive Feature Selection and Regression.Neurocomputing · 2016Article
- Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering.PloS one · 2015Article
- Article
- A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems.JMLR workshop and conference proceedings · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biomedical informatics. Most of the existing multi-task sparse feature learning algorithms are formulated as a convex sparse regularization problem, which is usually suboptimal, due to its looseness for approximating an [Formula: see text]-type regularizer. In this paper, we propose a non-convex formulation for multi-task sparse feature learning based on a novel regularizer. To solve the non-convex optimization problem, we propose a Multi-Stage Multi-Task Feature Learning (MSMTFL) algorithm. Moreover, we present a detailed theoretical analysis showing that MSMTFL achieves a better parameter estimation error bound than the convex formulation. Empirical studies on both synthetic and real-world data sets demonstrate the effectiveness of MSMTFL in comparison with the state of the art multi-task sparse feature learning algorithms.
Identifiers
24431924PMC3889129What OpenQuestion holds
Registered trials
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.