4 accepted papers
Partial Least Squares (PLS) has been gaining popularity as a multivariate data analysis tool due to its ability to cater for noisy, collinear and incomplete data-sets. However, most PLS solutions are designed as block-based algorithms, rendering them unsuitable for environments with streaming data a…
A resource effective extension to the class of distributed real-time diffusion Kalman filters is proposed. The proposed scheme removes the need to share measurement variables explicitly, by sharing only the state estimates and state error covariance matrices which implicitly contain the information…
Widely linear processing has been shown to be superior to the traditional strictly linear processing in quaternion minimum mean square error (MMSE) estimation. However, a quantifiable performance difference between strictly and widely linear processing and the relationship between the performance an…
Current approaches to the mean-square analyses of the complex-least-mean-square (CLMS) and augmented CLMS (ACLMS) algorithms can be challenging due to the difficulty in diagonalising the augmented covariance matrix. By employing the recently introduced approximate uncorrelating transform (AUT), whic…