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

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.1K
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...
7.1K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

501
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....
501
Distance Problem01:29

Distance Problem

215
When an object's velocity changes over time, the total distance traveled can be determined by summing small displacement intervals over short increments. This approach approximates the true distance through numerical summation and the use of integral calculus. An estimate of the total displacement can be obtained by measuring velocity at regular intervals and multiplying each value by the corresponding time step.If a runner accelerates over the first three seconds of a race, speed measurements...
215
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.0K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
4.0K
Reducing Line Loss01:18

Reducing Line Loss

502
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
502
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

1.6K
Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Continual Diffuser (CoD): Mastering Continual Offline RL With Experience Rehearsal.

IEEE transactions on neural networks and learning systems·2025
Same author

Task-Distributionally Robust Data-Free Meta-Learning.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Adaptive Batch Size Time Evolving Stochastic Gradient Descent for Federated Learning.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

GLC++: Source-Free Universal Domain Adaptation Through Global-Local Clustering and Contrastive Affinity Learning.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Toward the Flatter Landscape and Better Generalization in Federated Learning Under Client-Level Differential Privacy.

IEEE transactions on pattern analysis and machine intelligence·2025
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Apr 26, 2026

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

2.1K

Minimizing nearest neighbor classification error for nonparametric dimension reduction.

Wei Bian, Tianyi Zhou, Aleix M Martinez

    IEEE Transactions on Neural Networks and Learning Systems
    |July 23, 2014
    PubMed
    Summary
    This summary is machine-generated.

    Minimizing nearest neighbor classification error (MNNE) is a superior criterion for supervised linear dimension reduction (SLDR). This new method outperforms existing techniques by better approximating the Bayes optimal criterion.

    More Related Videos

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    6.3K
    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    7.0K

    Related Experiment Videos

    Last Updated: Apr 26, 2026

    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

    2.1K
    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    6.3K
    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    7.0K

    Area of Science:

    • Machine Learning
    • Pattern Recognition
    • Data Science

    Background:

    • Supervised linear dimension reduction (SLDR) aims to find a lower-dimensional representation of data while preserving class separability.
    • Existing SLDR criteria, such as maximizing mutual information, may not optimally reflect classification performance.
    • There is a need for SLDR methods that directly optimize criteria relevant to classification accuracy.

    Purpose of the Study:

    • To introduce and validate Minimizing Nearest Neighbor Classification Error (MNNE) as a novel criterion for SLDR.
    • To demonstrate that MNNE serves as a better proxy for the Bayes optimal classifier compared to maximizing mutual information.
    • To develop a practical, nonparametric algorithm for implementing MNNE-based SLDR.

    Main Methods:

    • Theoretical analysis proving MNNE's superiority over mutual information as a proxy for the Bayes optimal criterion.
    • Development of a nonparametric algorithm for MNNE using kernel density estimation.
    • Empirical evaluation of the proposed MNNE algorithm on benchmark datasets.

    Main Results:

    • MNNE is theoretically shown to be a more favorable criterion for SLDR than maximizing mutual information.
    • A novel nonparametric algorithm for MNNE-based SLDR is successfully derived.
    • Experimental results demonstrate the superior performance of MNNE over existing nonparametric SLDR methods.

    Conclusions:

    • Minimizing Nearest Neighbor Classification Error (MNNE) is an effective criterion for supervised linear dimension reduction.
    • The proposed nonparametric MNNE algorithm offers a practical and superior approach to SLDR.
    • This work advances SLDR by providing a criterion more directly aligned with classification performance.