Sentence examples for matrix estimation problem from inspiring English sources

In the first part, we investigate the scatter matrix estimation problem.

Before passing to discuss the scatter matrix estimation problem in complex t-distributed data, few remarks are needed.

Despite many studies made previously about traffic matrix estimation problem, it is a significant challenging to obtain its reliable and accurate solution.

Motivated by this issue, in this paper, we investigate the traffic matrix estimation problem in an IP-over-WDM backbone network.

In Section 3, the scatter matrix estimation problem is introduced and the application of the matched, mismatched, and robust approaches extensively analyzed.

The results of a number of experiments on range image segmentation and fundamental matrix estimation problems are presented.

Furthermore, a simplified method for estimating the eigenphone matrix in case of diagonal covariance matrices is derived, and a unified framework for solving various regularized matrix estimation problems is presented.

A unified framework for solving the various regularized matrix estimation problems is presented, and the performances of these regularization methods, including two combinations of them, i.e., elastic net and sparse group lasso, are compared for a supervised speaker adaptation task as well as an unsupervised speaker adaptation task using varying adaptation data.

The proposed algorithm, which is based on row-sparse matrix recovery for DOA estimation problem, not only handles multiple measurements but also converges significantly faster in comparison to other conventional compressed sensing algorithms.

In this paper, instead of estimating matrix V, we solve the DOA estimation problem efficiently by recovering a SIV which is used to represent the location of sources.

In Section "The singular problem of small sample inverse covariance matrix estimation", a method to solve the singular problem is provided, and the experiment result is shown in Figure 13. Figure 12 Analysis of correlation matrix estimation.