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)
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 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)
—We present approximations for eigenvalues of auto-correlation matrices in the presence of noncentral and signal-dependent noise as a function of eigenvalues of noiseless input. We derive error bounds for the approximations and discuss their properties. The results of the eigenanalysis are applied to the study of first-order polarization mode dispersion for(More)
—Detection of a given target or set of targets from observed data is a problem countered in many applications. Regardless of the algorithm selected, detection performance can be severely degraded when the subspace defined by the target data set is singular or ill conditioned. High correlations between target components and their linear combinations lead to(More)
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