Learning incoherent sparse and low-rank patterns from multiple tasks

By Jianhui Chen, Ji Liu, Jieping Ye. 2010

162 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Mathematical Physics, Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Qi Tian
2. Shiliang Zhang
3. Yu Zhang
4. Qiang Yang
5. Yu Zhang
6. Wen Gao
7. Jiayu Zhou
8. Chi Su

In Computational Mechanics

1. Qiang Yang
2. Yu Zhang
3. Lei Han
4. Yu Zhang
5. Yu Zhang
6. Jiayu Zhou
7. Dacheng Tao
8. James T. Kwok

In Mathematical Physics

1. Yu Zhang
2. Jiayu Zhou
3. Yu Zhang
4. Qiang Yang
5. James T. Kwok
6. Qiang Zhou
7. Leon Wenliang Zhong
8. Qi Zhao

In Representation & Self-Supervised Learning

1. Qiang Yang
2. Yu Zhang
3. Yu Zhang
4. Lei Han
5. Yu Zhang
6. Dacheng Tao
7. Jiayu Zhou
8. Qiang Zhou

Citations in the corpus

• An Overview of Multi-task Learning (2018, background): “For V , h ( V ) makes it sparse via the (cid:2) 1 norm in [53,54] and column-sparse via the (cid:2) 2,1 norm in [55,56], while in [57], h ( V ) penalizes the complexity of V via the squared Frobenius norm.”
• A Survey on Multi-Task Learning (2017, methodology): “Zhang [221] extends the conventional stability to the MTL setting by proving a generalized McDiarmids inequality and uses the proposed multi-task stability to analyze the trace norm regularization [49] in the low-rank approach and a decomposition model in [101] (i.”
• Multi-Task Learning and Algorithmic Stability (2015, methodology): “…different ways (Jacob, Bach, and Vert 2008; Zhang and Yeung 2010a; Solnon, Arlot, and Bach 2012; Zhang and Yeung 2013; 2014), decomposition methods (Chen, Liu, and Ye 2010; Jalali et al. 2010; Lozano and Swirszcz 2012; Zweig and Weinshall 2013) which decomposes the model parameters into two or…”
• Learning Multi-Level Task Groups in Multi-Task Learning (2015, background): “…(Chen, Zhou, and Ye 2011; Gong, Ye, and Zhang 2012), some assume that the model parameters of all the tasks lies in a low dimensional subspace (Ando and Zhang 2005; Chen, Liu, and Ye 2010; Chen, Zhou, and Ye 2011), some works learn the task relations (Zhang, Yeung, ∗Both authors contribute equally.”
• Multi-Task Learning with Low Rank Attribute Embedding for Person Re-Identification (2015, background): “Since tasks have shared information and each task also has specific structure, similar to [6], we assume W is composed of a low rank matrix shared by all tasks and a task-specific sparse component representing the incoherence introduced by individual tasks.”
• Online Multi-Task Learning Framework for Ensemble Forecasting (2017, background): “(a) Low-rank representation: This category of methods assumes that the models for different tasks share a low-rank representation, either additively [18], [19], [20] or multiplicatively [21], [22], [23].”
• Sparse Bayesian Multi-Task Learning (2011, methodology): “In this section, we evaluate the proposed model on two data sets: Yeast [17] and Scene [9], which have been widely used as testbeds to evaluate multi-task learning approaches [28, 29, 15].”
• Learning active facial patches for expression analysis (2012, background): “Recently, Chen [6] provided a faster solution to”
• On Better Exploring and Exploiting Task Relationships in Multitask Learning: Joint Model and Feature Learning (2018, methodology): “We compare our MTMF with two single-task learning methods—L2-norm regularized regression (L2-R) and L1-norm regularized regression (L1-R), as well as five stateof-the-art MTL algorithms, including trace norm regularized multitask learning, low-rank regularized multitask learning with sparse structur”
• Flexible Clustered Multi-Task Learning by Learning Representative Tasks (2016, methodology): “APG has been extensively used to solve machine learning problems [11], [12], [24], [59], [60], [61] due to the optimal convergence rate among all first-order methods.”
• Parallel Multi-task Learning (2015, methodology): “We classify regularized multi-task algorithms into two main categories: learning with feature covariance [5], [7], [6], [8], [9], [10], [11], [19], [20], [21], [22] and learning with task relations [2], [3], [4], [13], [15], [14], [17], [18], [16].”
• Multi-Task Learning for Spatio-Temporal Event Forecasting (2015, methodology): “Different regularization/constraint terms capture different types of task relatedness [12, 1, 16, 11].”
• Multitask Matrix Completion for Learning Protein Interactions Across Diseases (2016, background): “There has been a lot of work on other low-rank models for multitask learning (Ando and Zhang, 2005; Ji and Ye, 2009; Chen et al., 2012, 2013).”
• Multi-Task Learning with Low Rank Attribute Embedding for Multi-Camera Person Re-Identification (2018, methodology): “Since information is shared among tasks and each task has a specific structure, similar to [54], we assume that W is composed of a low rank matrix shared by all tasks and a task-specific sparse component representing the incoherence caused by individual tasks.”
• Convex Multitask Learning with Flexible Task Clusters (2012, methodology): “…in a Bayesian model (Yu et al., 2005; Zhang & Schneider, 2010; Zhang & Yeung, 2010); that the data follows a dirty model (Jalali et al., 2010); that most of the tasks lie in a low-dimensional subspace (Ando & Zhang, 2005; Chen et al., 2010), or that outlier tasks are present (Chen et al., 2011a).”
• GSpartan: a Geospatio-Temporal Multi-task Learning Framework for Multi-location Prediction (2016, background): “One of the fields is designated as the response variable we are interested in predicting, while the rest are considered predictor variables.”