Jove
Visualize
Contact Us

Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Regression Analysis01:11

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Correlation and Regression00:53

Correlation and Regression

In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

ChatTracker: Enhancing Visual Tracking via LLM-Driven Iterative Description Refinement.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Box2Mask: Box-Supervised Instance Segmentation via Level-Set Evolution.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Continual Learning, Fast and Slow.

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

Cross-Modal Graph With Meta Concepts for Video Captioning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2022
Same author

Learning Structural Representations for Recipe Generation and Food Retrieval.

IEEE transactions on pattern analysis and machine intelligence·2022
Same author

Collaborative Refining for Person Re-Identification With Label Noise.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2021
Same journal

Strategic Ability Updating in Concurrent Games by Coalitional Commitment.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2015
Same journal

Meta-Analysis of the First Facial Expression Recognition Challenge.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Adjustable model-based fusion method for multispectral and panchromatic images.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Face Feature Weighted Fusion Based on Fuzzy Membership Degree for Video Face Recognition.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

A New Adaptive Fast Cellular Automaton Neighborhood Detection and Rule Identification Algorithm.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Human-arm-and-hand-dynamic model with variability analyses for a stylus-based haptic interface.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Robust regularized kernel regression.

Jianke Zhu1, Steven C H Hoi, Michael Rung-Tsong Lyu

  • 1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong. jkzhu@cse.cuhk.edu.hk

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|November 22, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel primal formulation for robust regularized kernel regression, offering improved efficiency and implementation ease over dual methods. The approach effectively handles noisy data and optimizes bias terms for better performance.

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Statistical Modeling
  • Data Science

Background:

  • Robust regression is essential for handling noisy datasets in practical applications.
  • Existing robust kernel regression methods often rely on computationally intensive dual formulations solved by quadratic programming.

Purpose of the Study:

  • To develop a more efficient and implementable primal formulation for robust regularized kernel regression.
  • To address limitations of previous dual-formulation approaches.
  • To incorporate bias term optimization and extend to various loss functions.

Main Methods:

  • A new primal formulation for robust regularized kernel regression within the framework of regularization networks.
  • Direct optimization of the primal problem, avoiding complex quadratic programming solvers.
  • Extension of the method to noise-reliable loss functions like Huber-epsilon insensitive loss.

Main Results:

  • The primal formulation achieves comparable regression performance to dual methods.
  • The primal approach demonstrates superior efficiency and ease of implementation.
  • The method effectively optimizes the bias term and generalizes to different loss functions.
  • Experimental results on artificial and real datasets confirm the method's effectiveness and efficiency.

Conclusions:

  • The proposed primal formulation offers a significant advancement in robust regularized kernel regression.
  • This method provides a more practical and computationally efficient alternative to existing techniques for noisy data analysis.