Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Extraction: Advanced Methods

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...

Extraction: Partition and Distribution Coefficients

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...

Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

Linear Approximation in Frequency Domain

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.

Residuals and Least-Squares Property

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...

You might also read

Related Articles

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

Sort by

Same author

Impact of age and clinical factors on the feasibility of mobile digital monitoring in people at risk of suicide.

PloS one·2026

Same author

Digital relapse prevention plan for substance use disorders: study protocol for a multicentre randomised controlled trial.

BMJ health & care informatics·2025

Same author

Impact of Digital Safety Plan Activation on Subsequent Emergency Departments Visits Following an Initial Suicide Attempt: Quasi-Experimental Study.

JMIR mental health·2025

Same author

Feasibility of digital monitoring in patients receiving ambulatory psychiatric care.

Journal of psychiatric research·2025

Same author

Emotion Forecasting: A Transformer-Based Approach.

Journal of medical Internet research·2025

Same author

Reflections on the Potential and Risks of AI for Scientific Article Writing after the AI Endorsement by Some Scientific Publishers: Focusing on Scopus AI.

Actas espanolas de psiquiatria·2025

Search research articles

Related Experiment Video

Updated: Jul 8, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Maximization of mutual information for supervised linear feature extraction.

Jose Miguel Leiva-Murillo¹, Antonio Artés-Rodríguez

¹Department of Signal Theory and Communications, Universidad Carlos III, Madrid 28911, Spain. leiva@ieee.org

IEEE Transactions on Neural Networks

|January 29, 2008

Summary

This study introduces a new linear feature extraction method maximizing mutual information (MI) for classification. The approach outperforms existing techniques, especially for complex, nonlinear data boundaries.

Related Experiment Videos

Last Updated: Jul 8, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Machine Learning
Pattern Recognition
Data Science

Background:

Supervised feature extraction is crucial for classification tasks.
Existing methods may struggle with complex, nonlinear data distributions.
Maximizing information-theoretic measures is a promising direction for feature learning.

Purpose of the Study:

To propose a novel linear feature extraction scheme for classification.
To maximize the mutual information (MI) between extracted features and class labels.
To develop an efficient optimization method for MI maximization.

Main Methods:

The core method maximizes the sum of mutual information (MI) for individual features as an approximation.
A component-by-component gradient-ascent algorithm is employed for MI maximization.
The approach is inspired by gradient-based entropy optimization techniques used in Independent Component Analysis (ICA).

Main Results:

The proposed method demonstrates competitive performance against existing supervised feature extraction techniques.
The novel scheme significantly outperforms current methods when class boundaries are strongly nonlinear.
Simulation results validate the effectiveness of the MI maximization approach.

Conclusions:

The presented linear feature extraction method offers a robust alternative for classification.
The technique shows particular strength in handling datasets with complex, nonlinear class separations.
Maximizing mutual information provides an effective strategy for supervised feature learning.