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Jin Tang - One of the best experts on this subject based on the ideXlab platform.

  • using Eigen Decomposition method for weighted graph matching
    International Conference on Intelligent Computing, 2007
    Co-Authors: Guoxing Zhao, Bin Luo, Jin Tang
    Abstract:

    In this paper, Umeyama's Eigen-Decomposition approach to weighted graph matching problems is critically examined. We argue that Umeyama's approach only guarantees to work well for graphs that satisfy three critical conditions: (1) The pair of weighted graphs to be matched must be nearly isomorphic; (2) The Eigenvalues of the adjacency matrix of each graph have to be single and isolated enough to each other; (3) The rows of the matrix of the corresponding absolute Eigenvetors cannot be very similar to each other. For the purpose of matching general weighted graph pairs without such imposed constraints, we shall propose an approximate formula with a theoretical guarantee of accuracy, from which Umeyama's formula can be deduced as a special case. Based on this approximate formula, a new algorithm for matching weighted graphs is developed. The experimental results demonstrate great improvements to the accuracy of weighted graph matching.

  • ICIC (1) - Using Eigen-Decomposition method for weighted graph matching
    Lecture Notes in Computer Science, 1
    Co-Authors: Guoxing Zhao, Bin Luo, Jin Tang
    Abstract:

    In this paper, Umeyama's Eigen-Decomposition approach to weighted graph matching problems is critically examined. We argue that Umeyama's approach only guarantees to work well for graphs that satisfy three critical conditions: (1) The pair of weighted graphs to be matched must be nearly isomorphic; (2) The Eigenvalues of the adjacency matrix of each graph have to be single and isolated enough to each other; (3) The rows of the matrix of the corresponding absolute Eigenvetors cannot be very similar to each other. For the purpose of matching general weighted graph pairs without such imposed constraints, we shall propose an approximate formula with a theoretical guarantee of accuracy, from which Umeyama's formula can be deduced as a special case. Based on this approximate formula, a new algorithm for matching weighted graphs is developed. The experimental results demonstrate great improvements to the accuracy of weighted graph matching.

Ruikang K. Wang - One of the best experts on this subject based on the ideXlab platform.

  • dynamic imaging and quantification of subcellular motion with Eigen Decomposition optical coherence tomography based variance analysis
    Journal of Biophotonics, 2019
    Co-Authors: Peijun Tang, Yuandong Li, Ruikang K. Wang
    Abstract:

    : The dynamic properties of subcellular organism are important biomarkers of the health. Imaging subcellular level dynamics provides effective solutions for evaluating cell metabolism and testing the responses of cells to pathogens and drugs in pharmaceutical engineering. In this paper, we demonstrate an innovative approach to contrast the subcellular motion by using Eigen Decomposition (ED)-based variance analysis of time-dependent complex optical coherence tomography signals. This method reveals a superior advantage of contrast to noise ratio when compared with the approach that employs intensity decorrelation. Furthermore, the Eigen values derived from ED processing are calculated and applied to assess the power ratios of complex signal invariance that decreases exponentially along time dimension. The validation experiments are performed on the patterned samples of yeast powder mixed with gelatin/TiO2 water solution. Additionally, the proposed method is used to image mouse cerebral cortex in normal and pathological conditions, suggesting the practicality of variance power mapping in analyzing cortical neural activities. The technique promises efficient measurement of subcellular motions with high sensitivity and high throughput for in vivo and in situ applications.

  • Mapping and Quantitating Penetrating Vessels in Cortical Brain Using Eigen-Decomposition of OCT Signals and Subsequent Principal Component Analysis
    IEEE Journal of Selected Topics in Quantum Electronics, 2019
    Co-Authors: Wei Wei, Anthony J. Deegan, Ruikang K. Wang
    Abstract:

    Penetrating vessels bridge the mesh of communicating vessels on the surface of the cortex with the subsurface microvascular beds that feed the underlying neural tissue. Their accurate identification in vivo is important in the investigations of neural degenerative diseases, e.g., Alzheimer's disease and stroke. Here, we propose an efficient method to automatically map cortical penetrating vessels based on an Eigen decompensation analysis of the optical coherence tomography (OCT) and OCT angiographic signals. We first project the ensemble of repeated OCT signals into a feature space that represents the power spectral components of Eigenvectors through a well-known Eigen-Decomposition method. A principal component analysis is then applied to the spectral components to identify penetrating vessels. We find that the spectral components of OCT signals captured from a cortical brain tissue follow a subtle logistic distribution, which is, however, broken down if there are penetrating vessels. Such feature allows for an automatic mapping of penetrating arterioles and ascending venules from large volume OCT-scan datasets, accordingly contributing to the topological and morphological analyses of cortical microvasculature in the functioning brains. To demonstrate the utility of the proposed method, an OCT angiography imaging platform is used to show the functional behavior of penetrating blood flows before and after an ischemic insult in an established middle cerebral artery occlusion (MCAO) model of rodent, where an average of 41% reduction in penetrating vessel density ( n = 5 animals) was observed in the ischemic region post-MCAO.

  • Dynamic laser speckle angiography achieved by Eigen‐Decomposition filtering
    Journal of Biophotonics, 2016
    Co-Authors: Ruikang K. Wang
    Abstract:

    A new approach is proposed for statistically analysis of laser speckle signals emerged from a living biological tissue based on Eigen-Decomposition to separate the dynamic speckle signals due to moving blood cells from the static speckle signals due to static tissue components, upon which to achieve angiography of the interrogated tissue in vivo. The proposed approach is tested by imaging mouse ear pinna in vivo, demonstrating its capability of providing detailed microvascular networks with high contrast, and high temporal and spatial resolutions. It is expected to provide further opportunities for laser speckle imaging in the biomedical and clinical applications where microvascular response to certain stimulus or tissue injury is of interest.

  • Highly efficient Eigen Decomposition based statistical optical microangiography
    Quantitative imaging in medicine and surgery, 2016
    Co-Authors: Qinqin Zhang, Jingang Wang, Ruikang K. Wang
    Abstract:

    Background: To overcome the drawbacks of the voxel-based Eigen-Decomposition (vED) approach to achieve the purpose of imaging blood flow in living tissue in real time. Methods: A highly efficient and practical method for contrasting in vivo blood flow by applying Eigen-Decomposition (ED) filter on repeated complex-valued optical coherence tomography (OCT) B-scans Eigen-Decomposition (bED) is proposed. We first present basic mathematics for bED. We then validate the bED through imaging cerebral blood flow in a mouse model. Results: Through evaluating signal to noise ratio, contrast and vessel connectivity, it is found that the proposed method can better contrast blood flow with drastic saving on computational power when compared with traditional ED approach where the filtering is applied on the basis of pixel by pixel or voxel by voxel. Conclusions: The bED is practically feasible to realize real time OCT angiography. In addition, the proposed ED approach is equally applicable to the operations on repeated A-scans or volumetric scans.

Guoxing Zhao - One of the best experts on this subject based on the ideXlab platform.

  • using Eigen Decomposition method for weighted graph matching
    International Conference on Intelligent Computing, 2007
    Co-Authors: Guoxing Zhao, Bin Luo, Jin Tang
    Abstract:

    In this paper, Umeyama's Eigen-Decomposition approach to weighted graph matching problems is critically examined. We argue that Umeyama's approach only guarantees to work well for graphs that satisfy three critical conditions: (1) The pair of weighted graphs to be matched must be nearly isomorphic; (2) The Eigenvalues of the adjacency matrix of each graph have to be single and isolated enough to each other; (3) The rows of the matrix of the corresponding absolute Eigenvetors cannot be very similar to each other. For the purpose of matching general weighted graph pairs without such imposed constraints, we shall propose an approximate formula with a theoretical guarantee of accuracy, from which Umeyama's formula can be deduced as a special case. Based on this approximate formula, a new algorithm for matching weighted graphs is developed. The experimental results demonstrate great improvements to the accuracy of weighted graph matching.

  • ICIC (1) - Using Eigen-Decomposition method for weighted graph matching
    Lecture Notes in Computer Science, 1
    Co-Authors: Guoxing Zhao, Bin Luo, Jin Tang
    Abstract:

    In this paper, Umeyama's Eigen-Decomposition approach to weighted graph matching problems is critically examined. We argue that Umeyama's approach only guarantees to work well for graphs that satisfy three critical conditions: (1) The pair of weighted graphs to be matched must be nearly isomorphic; (2) The Eigenvalues of the adjacency matrix of each graph have to be single and isolated enough to each other; (3) The rows of the matrix of the corresponding absolute Eigenvetors cannot be very similar to each other. For the purpose of matching general weighted graph pairs without such imposed constraints, we shall propose an approximate formula with a theoretical guarantee of accuracy, from which Umeyama's formula can be deduced as a special case. Based on this approximate formula, a new algorithm for matching weighted graphs is developed. The experimental results demonstrate great improvements to the accuracy of weighted graph matching.

Jian-feng Liu - One of the best experts on this subject based on the ideXlab platform.

  • Eigen Decomposition expedites longitudinal genome-wide association studies for milk production traits in Chinese Holstein
    Genetics Selection Evolution, 2018
    Co-Authors: Chao Ning, Dan Wang, Xianrui Zheng, Qin Zhang, Shengli Zhang, Raphael Mrode, Jian-feng Liu
    Abstract:

    AbstractBackgroundPseudo-phenotypes, such as 305-day yields, estimated breeding values or deregressed proofs, are usually used as response variables for genome-wide association studies (GWAS) of milk production traits in dairy cattle. Computational inefficiency challenges the direct use of test-day records for longitudinal GWAS with large datasets.ResultsWe propose a rapid longitudinal GWAS method that is based on a random regression model. Our method uses Eigen Decomposition of the phenotypic covariance matrix to rotate the data, thereby transforming the complex mixed linear model into weighted least squares analysis. We performed a simulation study that showed that our method can control type I errors well and has higher power than a longitudinal GWAS method that does not include time-varied additive genetic effects. We also applied our method to the analysis of milk production traits in the first three parities of 6711 Chinese Holstein cows. The analysis for each trait was completed within 1 day with known variances. In total, we located 84 significant single nucleotide polymorphisms (SNPs) of which 65 were within previously reported quantitative trait loci (QTL) regions.ConclusionsOur rapid method can control type I errors in the analysis of longitudinal data and can be applied to other longitudinal traits. We detected QTL that were for the most part similar to those reported in a previous study in Chinese Holstein. Moreover, six additional SNPs for fat percentage and 13 SNPs for protein percentage were identified by our method. These additional 19 SNPs could be new candidate quantitative trait nucleotides for milk production traits in Chinese Holstein.

  • Eigen Decomposition expedites longitudinal genome-wide association studies for milk production traits in Chinese Holstein.
    Genetics selection evolution : GSE, 2018
    Co-Authors: Chao Ning, Dan Wang, Xianrui Zheng, Qin Zhang, Shengli Zhang, Raphael Mrode, Jian-feng Liu
    Abstract:

    Pseudo-phenotypes, such as 305-day yields, estimated breeding values or deregressed proofs, are usually used as response variables for genome-wide association studies (GWAS) of milk production traits in dairy cattle. Computational inefficiency challenges the direct use of test-day records for longitudinal GWAS with large datasets. We propose a rapid longitudinal GWAS method that is based on a random regression model. Our method uses Eigen Decomposition of the phenotypic covariance matrix to rotate the data, thereby transforming the complex mixed linear model into weighted least squares analysis. We performed a simulation study that showed that our method can control type I errors well and has higher power than a longitudinal GWAS method that does not include time-varied additive genetic effects. We also applied our method to the analysis of milk production traits in the first three parities of 6711 Chinese Holstein cows. The analysis for each trait was completed within 1 day with known variances. In total, we located 84 significant single nucleotide polymorphisms (SNPs) of which 65 were within previously reported quantitative trait loci (QTL) regions. Our rapid method can control type I errors in the analysis of longitudinal data and can be applied to other longitudinal traits. We detected QTL that were for the most part similar to those reported in a previous study in Chinese Holstein. Moreover, six additional SNPs for fat percentage and 13 SNPs for protein percentage were identified by our method. These additional 19 SNPs could be new candidate quantitative trait nucleotides for milk production traits in Chinese Holstein.

Bin Luo - One of the best experts on this subject based on the ideXlab platform.

  • using Eigen Decomposition method for weighted graph matching
    International Conference on Intelligent Computing, 2007
    Co-Authors: Guoxing Zhao, Bin Luo, Jin Tang
    Abstract:

    In this paper, Umeyama's Eigen-Decomposition approach to weighted graph matching problems is critically examined. We argue that Umeyama's approach only guarantees to work well for graphs that satisfy three critical conditions: (1) The pair of weighted graphs to be matched must be nearly isomorphic; (2) The Eigenvalues of the adjacency matrix of each graph have to be single and isolated enough to each other; (3) The rows of the matrix of the corresponding absolute Eigenvetors cannot be very similar to each other. For the purpose of matching general weighted graph pairs without such imposed constraints, we shall propose an approximate formula with a theoretical guarantee of accuracy, from which Umeyama's formula can be deduced as a special case. Based on this approximate formula, a new algorithm for matching weighted graphs is developed. The experimental results demonstrate great improvements to the accuracy of weighted graph matching.

  • ICIC (1) - Using Eigen-Decomposition method for weighted graph matching
    Lecture Notes in Computer Science, 1
    Co-Authors: Guoxing Zhao, Bin Luo, Jin Tang
    Abstract:

    In this paper, Umeyama's Eigen-Decomposition approach to weighted graph matching problems is critically examined. We argue that Umeyama's approach only guarantees to work well for graphs that satisfy three critical conditions: (1) The pair of weighted graphs to be matched must be nearly isomorphic; (2) The Eigenvalues of the adjacency matrix of each graph have to be single and isolated enough to each other; (3) The rows of the matrix of the corresponding absolute Eigenvetors cannot be very similar to each other. For the purpose of matching general weighted graph pairs without such imposed constraints, we shall propose an approximate formula with a theoretical guarantee of accuracy, from which Umeyama's formula can be deduced as a special case. Based on this approximate formula, a new algorithm for matching weighted graphs is developed. The experimental results demonstrate great improvements to the accuracy of weighted graph matching.