An Informed Multitask Diffusion Adaptation Approach to Study Tremor in Parkinson's Disease

Monajemi, Sadaf; Eftaxias, K.; Sanei, Saeid; Ong, Sim‐Heng

doi:10.1109/jstsp.2016.2578878

Cited by 24 publications

(17 citation statements)

References 37 publications

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“…In the incremental update step, first the nodes exchange local data {y k,i , H k,i , R k,i } with their neighbors and at every time instant i compute ψ ψ ψ k,i ←x k,i|i−1 and P k,i ← P k,i|i−1 . Then, each node performs KF with the available data to obtain the intermediate estimates ψ ψ ψ k,i as given by (2).…”

Section: B Diffusion Kalman Filtering Algorithmmentioning

confidence: 99%

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Performance Analysis of Distributed Kalman Filtering With Partial Diffusion Over Noisy Network

Vahidpour

Rastegarnia

Latifi

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

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The performance of partial diffusion Kalman filtering (PDKF) algorithm for the networks with noisy links is studied here. A closed-form expression for the steady-state mean square deviation is then derived and theoretically shown that when the links are noisy, the communication-performance tradeoff, reported for the PDKF algorithm, does not hold. Additionally, optimal selection of combination weights is investigated and a combination rule along with an adaptive implementation is motivated. The results confirm the theoretical outcome.

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Section: B Diffusion Kalman Filtering Algorithmmentioning

confidence: 99%

“…To evaluate the relative variance combination rule (52), the nodes need to know the variance products, µ 2 lk , of their neighbors, which are often not available beforehand. Therefore, an adaptive combination rule is desirable, where individual nodes learn their combination coefficients (52) using the available data.…”

Section: Adaptive Solutionmentioning

confidence: 99%

Performance Analysis of Distributed Kalman Filtering With Partial Diffusion Over Noisy Network

Vahidpour

Rastegarnia

Latifi

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

Self Cite

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show abstract

“…The cost at node i, i.e., f i (x i ) may be some squared error or the negative log-likelihood (the former can be regarded as a special case of the latter when the noise is Gaussian) with respect to the local data observed by node i. The link cost g ij (x i , x j ) for a link (i, j) can be used to enforce similarity between neighbor nodes, e.g., x i − x j 2 2 in multitask adaptive networks in [19], [21]. February…”

Section: A the Statement Of The Problemmentioning

confidence: 99%

Distributed Linearized ADMM for Network Cost Minimization

Cao

Liu

2018

IEEE Trans. on Signal and Inf. Process. over Networks

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In this work, we study a generic network cost minimization problem, in which every node has a local decision vector to determine. Each node incurs a cost depending on its decision vector and each link also incurs a cost depending on the decision vectors of its two end nodes. All nodes cooperate to minimize the overall network cost. The formulated network cost minimization problem has broad applications in distributed signal processing and control over multi-agent systems. To obtain a decentralized algorithm for the formulated problem, we resort to the distributed alternating direction method of multipliers (DADMM). However, each iteration of the DADMM involves solving a local optimization problem at each node, leading to intractable computational burden in many circumstances. As such, we propose a distributed linearized ADMM (DLADMM) algorithm for network cost minimization. In the DLADMM, each iteration only involves closed-form computations and avoids local optimization problems, which greatly reduces the computational complexity compared to the DADMM. We prove that the DLADMM converges to an optimal point when the local cost functions are convex and have Lipschitz continuous gradients. Linear convergence rate of the DLADMM is also established if the local cost functions are further strongly convex. Numerical experiments are conducted to corroborate the effectiveness of the DLADMM and we observe that the DLADMM has similar convergence performance as DADMM does while the former enjoys much lower computational overhead. The impact of network topology, connectivity and algorithm parameters are also investigated through simulations.

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“…Note that the objective function in (14) is a convex quadratic function. Hence, the necessary and sufficient condition for optimality of problem (14) is that the gradient of the objective function vanishes. The gradient of the objective function, which is denoted as J k n (T ), with respect to x n and v n,i can be computed as follows:…”

Section: ) Updating X and Vmentioning

confidence: 99%

Decentralized Sparse Multitask RLS Over Networks

Cao

Liu

2017

IEEE Trans. Signal Process.

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Distributed adaptive signal processing has attracted much attention in the recent decade owing to its effectiveness in many decentralized real-time applications in networked systems. Because many natural signals are highly sparse with most entries equal to zero, several decentralized sparse adaptive algorithms have been proposed recently. Most of them is focused on the single task estimation problems, in which all nodes receive data associated with the same unknown vector and collaborate to estimate it. However, many applications are inherently multitask oriented and each node has its own unknown vector different from others'. The related multitask estimation problem benefits from collaborations among the nodes as neighbor nodes usually share analogous properties and thus similar unknown vectors. In this work, we study the distributed sparse multitask recursive least squares (RLS) problem over networks. We first propose a decentralized online alternating direction method of multipliers (ADMM) algorithm for the formulated RLS problem. The algorithm is simplified for easy implementation with closed-form computations in each iteration and low storage requirements. Moreover, to further reduce the complexity, we present a decentralized online subgradient method with low computational overhead. We theoretically analyze the convergence behavior of the proposed subgradient method and derive an error bound related to the network topology and algorithm parameters. The effectiveness of the proposed algorithms is corroborated by numerical simulations and an accuracy-complexity tradeoff between the proposed two algorithms is highlighted.

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An Informed Multitask Diffusion Adaptation Approach to Study Tremor in Parkinson's Disease

Cited by 24 publications

References 37 publications

Performance Analysis of Distributed Kalman Filtering With Partial Diffusion Over Noisy Network

Performance Analysis of Distributed Kalman Filtering With Partial Diffusion Over Noisy Network

Distributed Linearized ADMM for Network Cost Minimization

Decentralized Sparse Multitask RLS Over Networks

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