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

Aggregates Classification01:29

Aggregates Classification

1.0K
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
1.0K
Classification of Signals01:30

Classification of Signals

1.6K
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...
1.6K
Classification of Systems-II01:31

Classification of Systems-II

651
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
651
Classification of Systems-I01:26

Classification of Systems-I

742
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
742
2D NMR: Overview of Heteronuclear Correlation Techniques01:18

2D NMR: Overview of Heteronuclear Correlation Techniques

905
Heteronuclear correlation spectroscopy is an analytical technique that investigates the coupling between different types of nuclei, often a proton and an X-nucleus, such as carbon-13 or nitrogen-15. This method is commonly used in nuclear magnetic resonance (NMR) spectroscopy to gain insights into complex chemical compounds' structural and compositional aspects. A typical heteronuclear correlation spectrum displays X-nucleus chemical shifts on one axis and a proton spectrum on the other...
905
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

502
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....
502

You might also read

Related Articles

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

Sort by
Same author

Structural basis for the differential recognition of integrin αvβ3 by rhodostomin and trimucrin.

Communications biology·2026
Same author

Vibration Rolling, Non-Vibration Rolling, and Static Stretching for Delayed- Onset Muscle Soreness on Physiological Changes and Recovery of Athletic Performance in Runners.

Journal of sports science & medicine·2026
Same author

Artificial Intelligence Application in Cornea and External Diseases.

Diagnostics (Basel, Switzerland)·2025
Same author

Deep Learning-Based Quantification of Residual Blood Clots in Single-Use Dialyzers Using Bedside Mobile-Captured Images.

American journal of nephrology·2025
Same author

Effect of Knee Extensor Power on Knee Pain in Adults With or at Risk for Osteoarthritis: The Multicenter Osteoarthritis Study.

The Journal of rheumatology·2025
Same author

Gait Retraining to Reduce Tibial Acceleration Versus a Standard Walking Program for Reducing Knee Pain and Loading in Adults With Knee Osteoarthritis: A Randomized Feasibility Trial.

ACR open rheumatology·2025

Related Experiment Video

Updated: May 1, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

21.0K

Heterogeneous domain adaptation and classification by exploiting the correlation subspace.

Yi-Ren Yeh, Chun-Hao Huang, Yu-Chiang Frank Wang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 9, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new domain adaptation method using canonical correlation analysis (CCA) for cross-domain pattern recognition. The approach enhances classifier design, outperforming existing methods in various recognition tasks.

    Related Experiment Videos

    Last Updated: May 1, 2026

    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    21.0K

    Area of Science:

    • Machine Learning
    • Computer Vision
    • Pattern Recognition

    Background:

    • Cross-domain pattern recognition involves analyzing data from different sources.
    • Existing methods often struggle with domain shift challenges.
    • Canonical Correlation Analysis (CCA) offers a way to find joint representations.

    Purpose of the Study:

    • To develop a novel domain adaptation approach for cross-domain pattern recognition.
    • To improve the efficiency and reduce overfitting in Kernel CCA (KCCA).
    • To integrate domain adaptation capabilities directly into classifier design.

    Main Methods:

    • Utilizing a derived correlation subspace from CCA for cross-domain data association.
    • Advancing reduced kernel techniques for computationally efficient and robust KCCA.
    • Proposing a novel Support Vector Machine (SVM) with a correlation regularizer (correlation-transfer SVM).

    Main Results:

    • The proposed method demonstrates improved computational efficiency and reduced overfitting in KCCA.
    • Correlation-transfer SVM effectively incorporates domain adaptation into classification.
    • Successful application to diverse tasks including cross-view action recognition and image-text classification.

    Conclusions:

    • The novel domain adaptation and classification approach significantly outperforms state-of-the-art methods.
    • The method offers a robust solution for various cross-domain recognition challenges.
    • Exploiting domain transferability within the CCA subspace is key to improved performance.