Adaptive Multi-task Sparse Learning with an Application to fMRI Study

Chen, Xi; He, Jingrui; Lawrence, Richard D.; Carbonell, Jaime G.

doi:10.1137/1.9781611972825.19

Cited by 11 publications

(11 citation statements)

References 24 publications

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“…In particular, an ℓ 1 -norm penalization is imposed to induce network sparsity (Achard and Bullmore, 2007; Chen et al, 2012; Friedman et al, 2008) and a fused regularization is imposed to preserve the temporal smoothness by encouraging Θ ( k ) to have similar topology and correlations strengths to its adjoining networks (Tibshirani et al, 2005). FMGL was implemented using an in-house software.…”

Section: Methodsmentioning

confidence: 99%

Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification

Wee

Yang

Yap

et al. 2015

Brain Imaging and Behavior

162

136

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In conventional resting-state functional MRI (R-fMRI) analysis, functional connectivity is assumed to be temporally stationary, overlooking neural activities or interactions that may happen within the scan duration. Dynamic changes of neural interactions can be reflected by variations of topology and correlation strength in temporally correlated functional connectivity networks. These connectivity networks may potentially capture subtle yet short neural connectivity disruptions induced by disease pathologies. Accordingly, we are motivated to utilize disrupted temporal network properties for improving control-patient classification performance. Specifically, a sliding window approach is firstly employed to generate a sequence of overlapping R-fMRI sub-series. Based on these sub-series, sliding window correlations, which characterize the neural interactions between brain regions, are then computed to construct a series of temporal networks. Individual estimation of these temporal networks using conventional network construction approaches fails to take into consideration intrinsic temporal smoothness among successive overlapping R-fMRI subseries. To preserve temporal smoothness of R-fMRI sub-series, we suggest to jointly estimate the temporal networks by maximizing a penalized log likelihood using a fused sparse learning algorithm. This sparse learning algorithm encourages temporally correlated networks to have similar network topology and correlation strengths. We design a disease identification framework based on the estimated temporal networks, and group level network property differences and classification results demonstrate the importance of including temporally dynamic R-fMRI scan information to improve diagnosis accuracy of mild cognitive impairment patients.

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Section: Methodsmentioning

confidence: 99%

Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification

Wee

Yang

Yap

et al. 2015

Brain Imaging and Behavior

162

136

View full text Add to dashboard Cite

show abstract

“…We introduced multi-task lasso learning (MTL) [23,24] in which many learning tasks are improved at the same time. This method produces accurate predictions and is highly effective.…”

Section: Multitask Visual Space Learningmentioning

confidence: 99%

Functional magnetic resonance imaging-based brain decoding with visual semantic model

Saengpetch

Pipanmemekaporn

Kamolsantiroj

2020

IJECE

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The activity pattern of the brain has been activated to identify a person in mind. Using the function magnetic resonance imaging (fMRI) to decipher brain decoding is the most accepted method. However, the accuracy of fMRI-based brain decoder is still restricted due to limited training samples. The limitations of the brain decoder using fMRI are passed through the design features proposed for many label coding and model training to predict these characteristics for a particular label. Moreover, what kind of semantic features for deciphering the neurological activity patterns are unclear. In current work, a new calculation model for learning decoding labels that is consistent with fMRI activity responses. The approach demonstrates the proposed corresponding label's success in terms of accuracy, which is decoded from brain activity patterns and compared with conventional text-derived feature technique. Besides, experimental studies present a training model based on multi-tasking to reduce the problems of limited training data sets. Therefore, the multi-task learning model is more efficient than modern methods of calculation, and decoding features may be easily obtained.

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“…By simultaneously learning all tasks, MTL has shown great performance improvement in several related applications. Many existing MTL methods [2,8,9,15,17,20,26,46,51,52] are formulated as the regularized optimization problems with an empirical loss term of the training data plus a regularization term. Their contributions usually focus on designing meaningful regularization terms in order to capture the underlying commonality among tasks.…”

Section: Related Workmentioning

confidence: 99%

Multi-task Crowdsourcing via an Optimization Framework

Zhou

Lü

2019

ACM Trans. Knowl. Discov. Data

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The unprecedented amounts of data have catalyzed the trend of combining human insights with machine learning techniques, which facilitate the use of crowdsourcing to enlist label information both effectively and efficiently. One crucial challenge in crowdsourcing is the diverse worker quality, which determines the accuracy of the label information provided by such workers. Motivated by the observations that same set of tasks are typically labeled by the same set of workers, we studied their behaviors across multiple related tasks and proposed an optimization framework for learning from task and worker dual heterogeneity. The proposed method 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. Then, we propose an iterative algorithm to solve the optimization problem 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. We also prove that the gradient of the most time-consuming updating block is separable with respect to the workers, which leads to a randomized algorithm with faster speed. Moreover, we extend the learning framework to accommodate to the multi-class setting. Finally, we test the performance of our framework on several datasets, and demonstrate its superiority over state-of-theart techniques.

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Adaptive Multi-task Sparse Learning with an Application to fMRI Study

Cited by 11 publications

References 24 publications

Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification

Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification

Functional magnetic resonance imaging-based brain decoding with visual semantic model

Multi-task Crowdsourcing via an Optimization Framework

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