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

Force Classification01:22

Force Classification

1.4K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.4K
Classification of Signals01:30

Classification of Signals

773
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...
773
Classification of Leukocytes01:30

Classification of Leukocytes

2.4K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
2.4K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

35.3K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
35.3K
Classification of Systems-II01:31

Classification of Systems-II

219
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,
219
Classification of Systems-I01:26

Classification of Systems-I

285
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:
285

You might also read

Related Articles

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

Sort by
Same author

One-Pot Synthesis of PtBi-Co<sub>X</sub> Alloys for Electrochemical Nitrate Reduction to Ammonia.

Materials (Basel, Switzerland)·2026
Same author

2D Ruddlesden-Popper Perovskite (C<sub>6</sub>H<sub>5</sub>NH<sub>3</sub>)<sub>2</sub>CsPb<sub>2</sub>Cl<sub>7</sub> with Favorable Radiative Recombination and Field-Effect Transport.

Materials (Basel, Switzerland)·2026
Same author

Tunable Emission Peak Position and Enhanced Thermal Stability of CsPbBr<sub>3</sub> Quantum Dots via TMCS Ligand Exchange.

Materials (Basel, Switzerland)·2026
Same author

Serum matrix metalloproteinase-7 as a diagnostic and prognostic biomarker in primary biliary cholangitis.

Frontiers in medicine·2026
Same author

Phase boundary construction and multi-field synergy: multifunctional applications of TiO<sub>2</sub> conductive coatings.

Journal of colloid and interface science·2026
Same author

Coupling sulfion oxidation with hydrogen evolution via an amorphous NiMo sulfide for energy and resource recovery.

Journal of colloid and interface science·2026
Same journal

MVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation.

Knowledge-based systems·2026
Same journal

Denoising Diffusion Wavelet Models for Zero-shot Medical Image Translation.

Knowledge-based systems·2026
Same journal

Log-based sparse nonnegative matrix factorization for data representation.

Knowledge-based systems·2025
Same journal

Preserving bilateral view structural information for subspace clustering.

Knowledge-based systems·2025
Same journal

Global and Local Similarity Learning in Multi-Kernel Space for Nonnegative Matrix Factorization.

Knowledge-based systems·2025
Same journal

HeteroKGRep: Heterogeneous Knowledge Graph based Drug Repositioning.

Knowledge-based systems·2024
See all related articles

Related Experiment Video

Updated: Aug 29, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K

Learning discriminative representation for image classification.

Chong Peng1, Yang Liu1, Xin Zhang1

  • 1College of Computer Science and Technology, Qingdao University, China.

Knowledge-Based Systems
|September 5, 2022
PubMed
Summary
This summary is machine-generated.

This study presents a novel two-dimensional discriminative regression classifier for small-sample image data. The new method effectively exploits spatial information, outperforming existing techniques in accuracy and robustness.

Keywords:
2-dimensionalClassificationDiscriminativenessRidge regression

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K

Related Experiment Videos

Last Updated: Aug 29, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K

Area of Science:

  • Computer Vision
  • Machine Learning
  • Pattern Recognition

Background:

  • Small-sample image classification presents challenges due to limited data.
  • Existing methods often fail to fully utilize inherent spatial information in image data.
  • Vectorization of image data can lead to loss of crucial spatial context.

Purpose of the Study:

  • To introduce a novel classifier for small-sample image data.
  • To develop a method that leverages two-dimensional features for improved classification.
  • To enhance accuracy and robustness in image classification tasks.

Main Methods:

  • A two-dimensional discriminative regression approach is proposed.
  • The method estimates a discriminative representation from training examples.
  • Inherent spatial information is explicitly incorporated into the regression model.

Main Results:

  • The proposed classifier demonstrates superior performance compared to state-of-the-art methods.
  • Experimental results show significant improvements in classification accuracy.
  • The method exhibits enhanced robustness against noise corruption.

Conclusions:

  • The novel two-dimensional discriminative regression classifier is highly effective for small-sample image data.
  • Exploiting inherent spatial information leads to more robust and accurate image classification.
  • This approach offers a promising direction for future research in image recognition.