Wei Wang

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In this work, we introduce a simple and effective scheme to achieve joint blind source separation (BSS) of multiple datasets using multi-set canonical correlation analysis (M-CCA) [1]. We first propose a generative model of joint BSS based on the correlation of latent sources within and between datasets. We specify source separability conditions, and show(More)
We introduce non-negative matrix factorization with orthogonality constraints (NMFOC) for detection of a target spectrum in a given set of Raman spectra data. An orthogonality measure is defined and two different orthogonality constraints are imposed on the standard NMF to incorporate prior information into the estimation and hence to facilitate the(More)
We introduce non-negative matrix factorization with orthog-onality constraints (NMF-OC) for detection of a target spectrum in a given set of Raman spectra data. An orthogonal-ity measure is defined and two different orthogonality constraints are imposed on the standard NMF to incorporate prior information into the estimation and hence to facilitate the(More)
A detection approach, detection with a correlation bound (DCB), is introduced based on a linear mixture model. We use the upper bound of the correlation between the target and mixing components as the detection index, and derive the expression for this correlation bound using the observed data. The proposed method is an unsupervised approach and provides(More)
In this work, we propose a scheme for joint blind source separation (BSS) of multiple datasets using canonical correlation analysis (CCA). The proposed scheme jointly extracts sources from each dataset in the order of between-set source correlations. We show that, when sources are uncorrelated within each dataset and correlated across different datasets(More)
Electronic equalizers, which have been used widely in wireless and wireline communications, have recently been recognized as effective solutions for mitigating the impairments in the optical communications channel as well. Now with the increasing availability of voltage-tunable integrated circuits for high speed operation, equalizers , in particular those(More)
We present an unsupervised detection approach, detection with canonical correlation (DCC), for target detection based on a linear mixture model. Our aim is determining the existence of certain targets in a given mixture without specific information on the targets or the background. We use canonical correlations between the target set and the mixed(More)
A detection approach, detection with correlation bound (DCB), is introduced based on a linear mixture model. We use the upper bound of the correlation between the target and mixing components as the detection index, and derive the expression for this correlation bound using the observed data. The proposed method is more robust and provides better detection(More)
This paper proposes a vessel active contour model based on local intensity weighting and a vessel vector field. Firstly, the energy function we define is evaluated along the evolving curve instead of all image points, and the function value at each point on the curve is based on the interior and exterior weighted means in a local neighborhood of the point,(More)
We present a data-driven approach for target detection and identification based on a linear mixture model. Our aim is to determine the existence of certain targets in a mixture without specific information on the targets or the background, and to identify the targets from a given library. We use the maximum canonical correlation between the target set and(More)