In solving a robust version of regularized least squares with weighting, a certain scalar-valued optimization problem is required in order to determine the regularized robust solution and the corresponding robustified weighting parameters.
Since the formation of a hazy image is related to the transmission, the weight function in the regularized function should be associated with the transmission.
Second, from the regularized PFM, we reconstruct a position weight matrix (PWM) whose element is the log-odds score between the PFM and background model, defined by a zero-order Markov chain.
The aberrant trials in the training set would contribute minimally to the learning of optimal weights because we used a regularized classifier to reduce the effects of outliers [7].
The model parameters described above for CHAOS-5l were estimated from 753,996 scalar data and 3×741,440 vector data by means of a regularized iteratively reweighted least-squares algorithm using Huber weights, minimizing the cost function.
These model parameters are estimated by means of a regularized Iteratively Reweighted Least-Squares approach using Huber weights, minimizing the cost function (1 where m is the model vector, the residuals vector e = dobs − dmod is the difference between observation dobs and model prediction dmod, and is the data covariance matrix.
Optimal weights (w) were estimated by a regularized least square classifier [7], [8].
We estimated the weights of each gene using an L2 regularized logit regression model [ 7] with the R package glmnet.
Data was mapped into a higher dimension using RKS and weight matrix for optimizing the data generated using regularized least square (RLS).
This paper proposes a new variable regularized least-squares (VR-LS) algorithm by recursively constructing a weighting scalar of the regularized least-squares (LS) cost function.
