Integrating low-rank and group-sparse structures for robust multi-task learning

Chen, Jianhui; Zhou, Jiayu; Ye, Jieping

doi:10.1145/2020408.2020423

Cited by 285 publications

(188 citation statements)

References 25 publications

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“…We ran our experiments over five random repetitions of a 20/80% train/test ratio, find regularization parameters using cross-validation, and report results on the test set where regression outputs are computed by averaging frame-level scores for GIFs in each emotion. We adopt the normalized mean squared error (nMSE) used in previous studies [1,5] for our experiments. The nMSE is defined as the mean squared error (MSE) divided by the variance of the target vector and assures that the error is not biased toward models that over or under predict.…”

Section: Multi-task Emotion Regressionmentioning

confidence: 99%

Predicting Viewer Perceived Emotions in Animated GIFs

Jou

Bhattacharya

Chang

2014

Proceedings of the 22nd ACM International Conference on Multimedia

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Animated GIFs are everywhere on the Web. Our work focuses on the computational prediction of emotions perceived by viewers after they are shown animated GIF images. We evaluate our results on a dataset of over 3,800 animated GIFs gathered from MIT's GIFGIF platform, each with scores for 17 discrete emotions aggregated from over 2.5M user annotations -the first computational evaluation of its kind for content-based prediction on animated GIFs to our knowledge. In addition, we advocate a conceptual paradigm in emotion prediction that shows delineating distinct types of emotion is important and is useful to be concrete about the emotion target. One of our objectives is to systematically compare different types of content features for emotion prediction, including low-level, aesthetics, semantic and face features. We also formulate a multi-task regression problem to evaluate whether viewer perceived emotion prediction can benefit from jointly learning across emotion classes compared to disjoint, independent learning.

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Section: Multi-task Emotion Regressionmentioning

confidence: 99%

Predicting Viewer Perceived Emotions in Animated GIFs

Jou

Bhattacharya

Chang

2014

Proceedings of the 22nd ACM International Conference on Multimedia

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“…For example, [13][14][15][34][35][36] assume that there are a set of features (either in original space or in a transformed space) sharing for all tasks. There are also some multi-task learning algorithms using sparse constraints, such as 1 norm constraint [11] , 2, 1 norm constraint [37] , trace norm constraint [15,36] , and the combination of them such as 1 + 1 ,q norm multi-task learning [16] , sparse and low-rank multi-task learning [13] , robust multi-task learning using group sparse and low rank constraints [38] , robust multi-task feature learning [39] .…”

Section: Related Workmentioning

confidence: 99%

User attribute discovery with missing labels

Cong

Sun

Liu

et al. 2018

Pattern Recognition

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“…Different assumptions on task relatedness lead to various formulations. Examples include multi-task with joint feature learning [4,26] and sparse and low-rank multi-task learning [22], etc.…”

Section: Effectivenessmentioning

confidence: 99%

MultiC²: an Optimization Framework for Learning from Task and Worker Dual Heterogeneity

Zhou¹,

Lü²,

He³

2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Nowadays, crowdsourcing has been commonly used to enlist label information both effectively and efficiently. One major challenge in crowdsourcing is the diverse worker quality, which determines the accuracy of the label information provided by such workers. Motivated by the observation that in many crowdsourcing platforms, the same set of workers typically work on the same set of tasks, we propose to model the diverse worker quality by studying their behaviors across multiple related tasks. To this end, we propose an optimization framework named MultiC 2 for learning from task and worker dual heterogeneity. It uses a weight tensor to represent the workers' behaviors across multiple tasks, and seeks to find the optimal solution of the tensor by exploiting its structured information. We then propose an iterative algorithm to solve the optimization framework and analyze its computational complexity. To infer the true label of an example, we construct a worker ensemble based on the estimated tensor, whose decisions will be weighted using a set of entropy weight. Finally, we test the performance of MultiC 2 on various data sets, and demonstrate its superiority over state-of-the-art crowdsourcing techniques.

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Integrating low-rank and group-sparse structures for robust multi-task learning

Cited by 285 publications

References 25 publications

Predicting Viewer Perceived Emotions in Animated GIFs

Predicting Viewer Perceived Emotions in Animated GIFs

User attribute discovery with missing labels

MultiC²: an Optimization Framework for Learning from Task and Worker Dual Heterogeneity

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Integrating low-rank and group-sparse structures for robust multi-task learning

Cited by 285 publications

References 25 publications

Predicting Viewer Perceived Emotions in Animated GIFs

Predicting Viewer Perceived Emotions in Animated GIFs

User attribute discovery with missing labels

MultiC2: an Optimization Framework for Learning from Task and Worker Dual Heterogeneity

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Product

Resources

About

MultiC²: an Optimization Framework for Learning from Task and Worker Dual Heterogeneity