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Bar Graph01:07

Bar Graph

19.6K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
19.6K
Ogive Graph01:07

Ogive Graph

5.9K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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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...
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Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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Residual Plots01:07

Residual Plots

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A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
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Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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Related Experiment Video

Updated: Sep 15, 2025

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

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Discriminative graph regularized representation learning for recognition.

Jinshan Qi1, Rui Xu1,2

  • 1School of Computer Science and Technology, Huaiyin Normal University, Huaian, China.

Plos One
|July 17, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel discriminative graph regularized representation learning (DGRL) model for improved feature extraction. DGRL enhances generalization and discrimination by integrating global, local, and label structures for better recognition tasks.

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Area of Science:

  • Machine Learning
  • Computer Vision
  • Data Science

Background:

  • Feature extraction is crucial for machine learning applications.
  • Existing methods often treat dimension reduction and representation learning separately.
  • There is a need for models that capture complex data structures for enhanced recognition.

Purpose of the Study:

  • To propose a novel discriminative graph regularized representation learning (DGRL) model.
  • To integrate global, local, and label data structures into a unified framework.
  • To improve feature representation for generalization and discrimination in recognition tasks.

Main Methods:

  • Developed a Discriminative Graph Regularized Representation Learning (DGRL) model.
  • Integrated dimension reduction with ridge regression to capture subspace structures.
  • Introduced a graph regularizer utilizing local class information to enhance accuracy and prevent overfitting.
  • Proposed a kernel version (KDGRL) for nonlinear data using the kernel trick.
  • Provided theoretical derivations and parameter estimation procedures using cross-validation.

Main Results:

  • The DGRL model effectively incorporates global, local, and label structures.
  • The proposed methods demonstrate superior performance in benchmark experiments.
  • KDGRL successfully handles complex nonlinear data.
  • The framework unifies several existing approaches, clarifying their relationships.

Conclusions:

  • The novel DGRL and KDGRL models offer effective solutions for feature extraction.
  • These methods enhance generalization and discrimination capabilities for recognition tasks.
  • The integrated approach provides a robust framework for representation learning.