Multi-source transfer ELM-based Q learning

Pan, Jie; Wang, Xuesong; Cheng, Yuhu; Cao, Ge

doi:10.1016/j.neucom.2013.04.045

Cited by 27 publications

(12 citation statements)

References 19 publications

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“…These questions belong to the field of unsupervised transfer learning. Unsupervised transfer learning aims to improve the learning of one data set, called the target data, using the knowledge learnt in another data set, called the source data(Pan and Yang, 2009). Wang et al (2008) proposed a transferred discriminative analysis (TDA) algorithm to solve the transfer dimensionality reduction problem.…”

Section: Introductionmentioning

confidence: 99%

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Coupled Co-clustering-based Unsupervised Transfer Learning for the Integrative Analysis of Single-Cell Genomic Data

Zeng

Wangwu

Lin

2020

Preprint

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Unsupervised methods, such as clustering methods, are essential to the analysis of singlecell genomic data. Most current clustering methods are designed for one data type only, such as scRNA-seq, scATAC-seq or sc-methylation data alone, and a few are developed for the integrative analysis of multiple data types. Integrative analysis of multimodal singlecell genomic data sets leverages the power in multiple data sets and can deepen the biological insight. We propose a coupled co-clustering-based unsupervised transfer learning algorithm (coupleCoC) for the integrative analysis of multimodal single-cell data. Our proposed coupleCoC builds upon the information theoretic co-clustering framework. We applied coupleCoC for the integrative analysis of scATAC-seq and scRNA-seq data, sc-methylation and scRNA-seq data, and scRNA-seq data from mouse and human. We demonstrate that coupleCoC improves the overall clustering performance and matches the cell subpopulations across multimodal single-cell genomic data sets. The software and data sets are available at https://github.com/cuhklinlab/coupleCoC

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Section: Introductionmentioning

confidence: 99%

“…As a clustering version of the self-taught learning (Raina et al, 2007), Dai et al (2008) proposed a self-taught learning algorithm (STC) to learn a common feature space across data sets, which improves clustering for the target data. Details on transfer learning are provided in the survey by Pan and Yang (2009).…”

Section: Introductionmentioning

confidence: 99%

Coupled Co-clustering-based Unsupervised Transfer Learning for the Integrative Analysis of Single-Cell Genomic Data

Zeng

Wangwu

Lin

2020

Preprint

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“…Also, reinforcement learning (RL) can be accelerated by knowledge conversion [29], and agents learn new tasks faster and interact less with the environment. As a consequence, knowledge transfer reinforcement learning (KTRL) has been developed [30] through combining AI and behavior psychology [31] and is divided into behavior shift and information shift.…”

Section: Introductionmentioning

confidence: 99%

Reactive Power Optimization of Large-Scale Power Systems: A Transfer Bees Optimizer Application

Zhang

Yang

et al. 2019

Processes

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A novel transfer bees optimizer for reactive power optimization in a high-power system was developed in this paper. Q-learning was adopted to construct the learning mode of bees, improving the intelligence of bees through task division and cooperation. Behavior transfer was introduced, and prior knowledge of the source task was used to process the new task according to its similarity to the source task, so as to accelerate the convergence of the transfer bees optimizer. Moreover, the solution space was decomposed into multiple low-dimensional solution spaces via associated state-action chains. The transfer bees optimizer performance of reactive power optimization was assessed, while simulation results showed that the convergence of the proposed algorithm was more stable and faster, and the algorithm was about 4 to 68 times faster than the traditional artificial intelligence algorithms.

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“…Additionally, in the field of optimal control, adaptive dynamic programming (ADP) [18][19][20][21] is a significant and hot topic. Many data-driven and model-free methods based on ADP have been established [22][23][24][25][26][27][28][29][30][31][32]. Different from the above methods, virtual reference feedback tuning (VRFT), which is originally proposed by Guardabassi and Savaresi [33], provides a global solution to a model reference control problem with oneshot off-line data.…”

Section: Introductionmentioning

confidence: 99%

Data-driven controller design for general MIMO nonlinear systems via virtual reference feedback tuning and neural networks

et al. 2016

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Multi-source transfer ELM-based Q learning

Cited by 27 publications

References 19 publications

Coupled Co-clustering-based Unsupervised Transfer Learning for the Integrative Analysis of Single-Cell Genomic Data

Coupled Co-clustering-based Unsupervised Transfer Learning for the Integrative Analysis of Single-Cell Genomic Data

Reactive Power Optimization of Large-Scale Power Systems: A Transfer Bees Optimizer Application

Data-driven controller design for general MIMO nonlinear systems via virtual reference feedback tuning and neural networks

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