Adaptive regularized diffusion adaptation over multitask networks

Monajemi, Sadaf; Sanei, Saeid; Ong, Sim‐Heng; Sayed, Ali H.

doi:10.1109/mlsp.2015.7324358

Cited by 13 publications

(17 citation statements)

References 17 publications

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“…Algorithms 1 and 2 employ the same aggregation step in (29) and (45). Node k combines the intermediate estimates of its neighbors in the common subspace Θ without affecting the local contribution in the complementary subspace Θ ⊥ .…”

Section: B Node-specific Subspace Constraints With Norm-bounded Projmentioning

confidence: 99%

“…They derive a closedform expression of the proximal operator, and introduce a strategy that also allows each node to automatically set its inter-cluster cooperation weights. The works in [29], [30] propose alternative node clustering strategies. In a second scenario, it is assumed that there are parameters of global interest to all nodes in the network, a collection of parameters of common interest within sub-groups of nodes, and a set of parameters of local interest at each node.…”

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confidence: 99%

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Multitask Diffusion Adaptation Over Networks With Common Latent Representations

Chen

Richard

Sayed

2017

IEEE J. Sel. Top. Signal Process.

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Online learning with streaming data in a distributed and collaborative manner can be useful in a wide range of applications. This topic has been receiving considerable attention in recent years with emphasis on both single-task and multitask scenarios. In single-task adaptation, agents cooperate to track an objective of common interest, while in multitask adaptation agents track multiple objectives simultaneously. Regularization is one useful technique to promote and exploit similarity among tasks in the latter scenario. This work examines an alternative way to model relations among tasks by assuming that they all share a common latent feature representation. As a result, a new multitask learning formulation is presented and algorithms are developed for its solution in a distributed online manner. We present a unified framework to analyze the mean-square-error performance of the adaptive strategies, and conduct simulations to illustrate the theoretical findings and potential applications.

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Section: B Node-specific Subspace Constraints With Norm-bounded Projmentioning

confidence: 99%

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confidence: 99%

Multitask Diffusion Adaptation Over Networks With Common Latent Representations

Chen

Richard

Sayed

2017

IEEE J. Sel. Top. Signal Process.

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“…Incorporating this symmetric distribution and considering the spatial correlation of P300 in the system, motivates us to form a cooperative PF to enhance the accuracy of the tracking and estimation process. Cooperative approaches are of growing interest in problems with interconnected nodes due to their robustness, enhanced performance, and simplicity [39][40][41][42]. In these approaches, each node acquires its own observation (the P300 waveform here) and process it to track the unknown underlying parameters.…”

Section: Cooperative Particle Filteringmentioning

confidence: 99%

Cooperative Particle Filtering for Tracking ERP Subcomponents from Multichannel EEG

Monajemi

Jarchi

Ong

et al. 2017

Entropy

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Abstract:In this study, we propose a novel method to investigate P300 variability over different trials. The method incorporates spatial correlation between EEG channels to form a cooperative coupled particle filtering method that tracks the P300 subcomponents, P3a and P3b, over trials. Using state space systems, the amplitude, latency, and width of each subcomponent are modeled as the main underlying parameters. With four electrodes, two coupled Rao-Blackwellised particle filter pairs are used to recursively estimate the system state over trials. A number of physiological constraints are also imposed to avoid generating invalid particles in the estimation process. Motivated by the bilateral symmetry of ERPs over the brain, the channels further share their estimates with their neighbors and combine the received information to obtain a more accurate and robust solution. The proposed algorithm is capable of estimating the P300 subcomponents in single trials and outperforms its non-cooperative counterpart.

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“…[9][10][11][12][13][14][15][16][17] Solving cooperative learning problems that include multiple objectives is challenging because cooperation between agents with different objectives may lead to disastrous results. Nevertheless, there are important situations where agents in the network are interested in multiple objectives that are different from each other.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, there are important situations where agents in the network are interested in multiple objectives that are different from each other. [9][10][11][12][13][14][15][16][17] Solving cooperative learning problems that include multiple objectives is challenging because cooperation between agents with different objectives may lead to disastrous results. 16,17 One useful way to extract similarities among objectives is to formulate optimization problems based on information theoretic learning cost functions.…”

Section: Introductionmentioning

confidence: 99%

Adaptive multitask network based on maximum correntropy learning algorithm

Hajiabadi

Hodtani

Khoshbin

2017

Adaptive Control & Signal

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Adaptive networks solve distributed optimization problems in which all agents of the network are interested to collaborate with their neighbors to learn a similar task. Collaboration is useful when all agents seek a similar task. However, in many applications, agents may belong to different clusters that seek dissimilar tasks. In this case, nonselective collaboration will lead to damaging results that are worse than noncooperative solution. In this paper, we contribute in problems that several clusters of interconnected agents are interested in learning multiple tasks. To address multitask learning problem, we consider an information theoretic criterion called correntropy in a distributed manner providing a novel adaptive combination policy that allows agents to learn which neighbors they should cooperate with and which other neighbors they should reject. In doing so, the proposed algorithm enables agents to recognize their clusters and to achieve improved learning performance compared with noncooperative strategy. Stability analysis in the mean sense and also a closed-form relation determining the network error performance in steady-state mean-square-deviation is derived. Simulation results illustrate the theoretical findings and match well with theory. KEYWORDSadaptive network, correntropy criterion, distributed processing, multitask learning 1 1232

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Adaptive regularized diffusion adaptation over multitask networks

Cited by 13 publications

References 17 publications

Multitask Diffusion Adaptation Over Networks With Common Latent Representations

Multitask Diffusion Adaptation Over Networks With Common Latent Representations

Cooperative Particle Filtering for Tracking ERP Subcomponents from Multichannel EEG

Adaptive multitask network based on maximum correntropy learning algorithm

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