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

Distance Corrections01:15

Distance Corrections

60
To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
60
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.8K
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...
7.8K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

121
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....
121
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
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...
6.3K
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.7K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.7K
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

329
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
329

You might also read

Related Articles

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

Sort by
Same author

Synthesis and characterization of a glutathione-responsive NIR-II fluorescent probe with Fenton-like reaction activity.

RSC advances·2026
Same author

Hyaluronic acid-modified nanoparticles for chemo/sonodynamic therapy: Maximizing antitumor efficacy through the induction of ferroptosis and apoptosis.

International journal of biological macromolecules·2026
Same author

Metabolic-inflammatory axis linking diabetes and sarcopenia: cross-population evidence and explainable ai-based risk modeling.

Acta diabetologica·2026
Same author

A wearable IMU-based framework for daily physical activity recognition and energy expenditure level classification in university students.

Frontiers in public health·2026
Same author

A Copper-Catalyzed Approach to Access (Het)Aryl/Alkenyl Selenoglycosides Employing Electrophilic Glycosyl Selenosulfonates.

Organic letters·2026
Same author

Pediatric liver transplantation for metabolic diseases: a single-center experience.

World journal of pediatrics : WJP·2026

Related Experiment Video

Updated: Aug 10, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.4K

Learning matrix factorization with scalable distance metric and regularizer.

Shiping Wang1, Yunhe Zhang2, Xincan Lin2

  • 1College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China; Guangdong Provincial Key Laboratory of Big Data Computing, The Chinese University of Hong Kong, Shenzhen 518172, China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 12, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep matrix factorization method using projected gradient descent. It enhances feature extraction from large datasets, overcoming limitations of traditional techniques.

Keywords:
Deep learningFeature representationLearnable auto-encoderMachine learningMatrix factorizationProjected gradient

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.0K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K

Related Experiment Videos

Last Updated: Aug 10, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.4K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.0K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K

Area of Science:

  • Machine Learning
  • Data Science
  • Artificial Intelligence

Background:

  • Matrix factorization is crucial for high-dimensional data analysis.
  • Traditional methods face challenges with generalization and computational cost on large datasets.

Purpose of the Study:

  • To propose a learnable deep matrix factorization method.
  • To address scalability and generalization issues in matrix factorization.

Main Methods:

  • Utilizing projected gradient descent for optimization.
  • Transforming constrained matrix factorization into neural network training.
  • Developing a novel activation function based on feasible set projection.

Main Results:

  • The proposed method learns multi-layer low-rank factors.
  • It can unify various existing matrix factorizations like SVD and NMF.
  • Demonstrated superior performance over state-of-the-art methods in experiments.

Conclusions:

  • The learnable deep matrix factorization offers improved performance and scalability.
  • This approach provides a flexible framework for diverse matrix factorization tasks.