Related Experiment Video
Updated: Nov 27, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Recursive Minimum Complex Kernel Risk-Sensitive Loss Algorithm
Guobing Qian1,2, Dan Luo1, Shiyuan Wang1
1College of Electronic and Information Engineering, Brain-inspired Computing & Intelligent Control of Chongqing Key Laboratory, Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing 400715, China.
Abstract:
The maximum complex correntropy criterion (MCCC) has been extended to complex domain for dealing with complex-valued data in the presence of impulsive noise. Compared with the correntropy based loss, a kernel risk-sensitive loss (KRSL) defined in kernel space has demonstrated a superior performance surface in the complex domain. However, there is no report regarding the recursive KRSL algorithm in the complex domain. Therefore, in this paper we propose a recursive complex KRSL algorithm called the recursive minimum complex kernel risk-sensitive loss (RMCKRSL). In addition, we analyze its stability and obtain the theoretical value of the excess mean square error (EMSE), which are both supported by simulations. Simulation results verify that the proposed RMCKRSL out-performs the MCCC, generalized MCCC (GMCCC), and traditional recursive least squares (RLS).
More Related Videos
07:15Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
06:45Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
Published on: October 28, 2022
Related Concept Videos
Reducing Line Loss
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Routh-Hurwitz Criterion II
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Routh-Hurwitz Criterion I
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...