Jove
Visualize
Contact Us
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 Concept Videos

Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

20.3K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
20.3K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

376
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
376
Frequency-dependent Selection01:21

Frequency-dependent Selection

24.4K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
24.4K
Fast Fourier Transform01:10

Fast Fourier Transform

1.1K
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
1.1K
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

795
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
795
Capillary Electrophoresis: Applications01:30

Capillary Electrophoresis: Applications

1.6K
Capillary electrophoretic separations offer various modes, each with unique applications. These modes include capillary zone electrophoresis, capillary gel electrophoresis, capillary array electrophoresis, capillary isoelectric focusing, capillary isotachophoresis, micellar electrokinetic chromatography, and capillary electrochromatography.
Capillary zone electrophoresis (CZE) separates ionic components based on their electrophoretic mobility. It has been used to separate proteins, amino acids,...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Nature-based Virtual Reality for Depression in Alzheimer's Disease: Protocol of a Pilot Randomized Controlled Trial.

Gerontology·2026
Same author

Image-Based Volatile Organic Compound Identification Using the Cosine Similarity Method.

ACS omega·2026
Same author

Robust Temporal Link Prediction in Dynamic Complex Networks via Stable Gated Models With Reinforcement Learning.

IEEE transactions on neural networks and learning systems·2024
Same author

The Impact of LiDAR Configuration on Goal-Based Navigation within a Deep Reinforcement Learning Framework.

Sensors (Basel, Switzerland)·2023
Same author

Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid.

Polymers·2023
Same author

SEEM: A Sequence Entropy Energy-Based Model for Pedestrian Trajectory All-Then-One Prediction.

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

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

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

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation.

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

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

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

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

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

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

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

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Mar 8, 2026

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.6K

Forward Selection Component Analysis: Algorithms and Applications.

Luca Puggini, Sean McLoone

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |January 20, 2017
    PubMed
    Summary
    This summary is machine-generated.

    Forward Selection Component Analysis (FSCA) offers improved interpretability for dimensionality reduction by simultaneously selecting variables. New FSCA variants with refinement steps demonstrate enhanced performance over PCA and Sparse PCA.

    More Related Videos

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    8.1K
    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    16.4K

    Related Experiment Videos

    Last Updated: Mar 8, 2026

    A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
    09:01

    A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

    Published on: May 7, 2014

    10.6K
    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    8.1K
    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    16.4K

    Area of Science:

    • Data Science
    • Machine Learning
    • Statistical Analysis

    Background:

    • Principal Component Analysis (PCA) is a standard dimensionality reduction technique.
    • PCA's principal components are linear combinations of all variables, hindering interpretation and root-cause analysis.

    Purpose of the Study:

    • Introduce and detail the Forward Selection Component Analysis (FSCA) algorithm.
    • Present novel FSCA variants with refinement steps to enhance performance.
    • Compare FSCA variants against PCA and Sparse PCA.

    Main Methods:

    • Detailed presentation of the FSCA algorithm.
    • Development of new FSCA variants incorporating a refinement step.
    • Comparative analysis of FSCA, PCA, and Sparse PCA on various applications.

    Main Results:

    • FSCA effectively performs simultaneous dimensionality reduction and variable selection.
    • FSCA variants with refinement steps show improved performance.
    • FSCA demonstrates low information loss compared to PCA and Sparse PCA.

    Conclusions:

    • FSCA is an effective technique for interpretable dimensionality reduction and variable selection.
    • Refinement steps significantly improve FSCA performance.
    • FSCA offers a valuable alternative to traditional PCA for complex datasets.