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

15.5K
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...
15.5K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

544
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
544
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

712
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
712
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

13.7K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
13.7K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.5K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.5K
Vector or Cross Product01:17

Vector or Cross Product

1.2K
1.2K

You might also read

Related Articles

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

Sort by
Same author

Assessment of Perceptions of Professionalism Among Faculty, Trainees, Staff, and Students in a Large University-Based Health System.

JAMA network open·2020
Same author

Inhalation airflow and ventilation efficiency in subject-specific human upper airways.

Respiratory physiology & neurobiology·2020
Same author

Adverse Effects of Low-Dose Methotrexate in a Randomized Double-Blind Placebo-Controlled Trial: Adjudicated Hematologic and Skin Cancer Outcomes in the Cardiovascular Inflammation Reduction Trial.

ACR open rheumatology·2020
Same author

Preparation and <i>in Vitro</i> Antitumor Study of Two-Dimensional Muscovite Nanosheets.

Langmuir : the ACS journal of surfaces and colloids·2020
Same author

Identification and Bioinformatic Assessment of circRNA Expression After <i>RMI1</i> Knockdown and Ionizing Radiation Exposure.

DNA and cell biology·2020
Same author

Pollution haven or halo? The role of the energy transition in the impact of FDI on SO2 emissions.

The Science of the total environment·2020

Related Experiment Video

Updated: Apr 30, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

21.0K

Multiview vector-valued manifold regularization for multilabel image classification.

Yong Luo, Dacheng Tao, Chang Xu

    IEEE Transactions on Neural Networks and Learning Systems
    |May 9, 2014
    PubMed
    Summary

    This study introduces multiview vector-valued manifold regularization (MV(3)MR) for computer vision. The method effectively integrates multiple image features, improving image classification accuracy by leveraging complementary visual information.

    More Related Videos

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    8.6K

    Related Experiment Videos

    Last Updated: Apr 30, 2026

    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    21.0K
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    8.6K

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Data Science

    Background:

    • Image datasets often contain multiple labels and visual features, requiring sophisticated analysis.
    • Existing tools fail to fully utilize label relationships or complementary feature information.
    • Multilabel image classification presents challenges due to inherent data complexity.

    Purpose of the Study:

    • To develop a novel method, multiview vector-valued manifold regularization (MV(3)MR), for integrating multiple features in image classification.
    • To address the limitations of current tools in handling label relationships and feature complementarity.
    • To enhance the accuracy and robustness of image classification models.

    Main Methods:

    • Introduced multiview vector-valued manifold regularization (MV(3)MR) by extending vector-valued function approaches.
    • Constructed matrix-valued kernels to explore multilabel structures in the output space.
    • Exploited complementary properties of different visual features and manifold regularization to discover intrinsic local geometry.

    Main Results:

    • Demonstrated the effectiveness of MV(3)MR on challenging datasets like PASCAL VOC'07 and MIR Flickr.
    • Achieved improved image classification performance by integrating multiple visual features.
    • Validated the method's ability to leverage complementary information for better classification outcomes.

    Conclusions:

    • MV(3)MR successfully integrates multiple features for enhanced image classification.
    • The proposed method effectively utilizes complementary visual information and label structures.
    • MV(3)MR offers a promising approach for complex image classification tasks in computer vision.