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

Chi-square Distribution01:10

Chi-square Distribution

How does one determine if bingo numbers are evenly distributed or if some numbers occurred with a greater frequency? Or if the types of movies people preferred were different across different age groups or if a coffee machine dispensed approximately the same amount of coffee each time. These questions can be addressed by conducting a hypothesis test. One distribution that can be used to find answers to such questions is known as the chi-square distribution. The chi-square distribution has...
Test for Homogeneity01:23

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...
Chi-square Analysis02:46

Chi-square Analysis

The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
Finding Critical Values for Chi-Square01:18

Finding Critical Values for Chi-Square

Consider a curve representing sample data drawn randomly from a normally distributed population. One must construct confidence intervals to estimate or to test a claim regarding the population standard deviation. For example, a 95% confidence interval covers 95% of the area under the curve, and the remaining 5% is equally distributed on either side of the curve. To achieve such confidence intervals, one must determine the critical values. The critical values are simply the values separating the...
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
¹H NMR Chemical Shift Equivalence: Homotopic and Heterotopic Protons01:03

¹H NMR Chemical Shift Equivalence: Homotopic and Heterotopic Protons

Protons in identical electronic environments within a molecule are chemically equivalent and have the same chemical shift. The replacement test is a useful tool to identify chemical equivalence and predict NMR spectra. A substituent replaces each of the protons being examined and the resulting molecules are compared. If the same molecule is obtained, the protons are equivalent or homotopic. Replacement of any hydrogens in ethane by chlorine yields chloroethane because all six protons are...

You might also read

Related Articles

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

Sort by
Same author

Unsupervised machine learning analysis to enhance risk stratification in patients with asymptomatic aortic stenosis.

European heart journal. Digital health·2026
Same author

AI-Enhanced Prediction of Aortic Stenosis Progression: Insights From the PROGRESSA Study.

JACC. Advances·2024
Same author

BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks.

Nature communications·2024
Same author

Enhancing Classification of liquid chromatography mass spectrometry data with Batch Effect Removal Neural Networks (BERNN).

Research square·2023
Same author

ResMLP: Feedforward Networks for Image Classification With Data-Efficient Training.

IEEE transactions on pattern analysis and machine intelligence·2022
Same author

RED++ : Data-Free Pruning of Deep Neural Networks via Input Splitting and Output Merging.

IEEE transactions on pattern analysis and machine intelligence·2022
Same journal

A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: May 28, 2026

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

Locality-Sensitive Hashing for Chi2 distance.

David Gorisse1, Matthieu Cord, Frederic Precioso

  • 1Yakaz Lab, 34 rue de Clery, Paris 75002, France. david@yakaz.com

IEEE Transactions on Pattern Analysis and Machine Intelligence
|October 5, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new Locality Sensitive Hashing (LSH) scheme for approximate nearest neighbors search using the L2 distance. It offers improved accuracy or speed for high-dimensional data, particularly in image retrieval.

More Related Videos

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

Related Experiment Videos

Last Updated: May 28, 2026

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

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

Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Nearest Neighbors search is crucial for many data analysis tasks.
  • Existing Euclidean Locality Sensitive Hashing (LSH) methods have limitations with certain distance metrics.
  • The Euclidean metric may not always yield the most relevant results for similarity measures like Earth-Mover Distance and L2 distance.

Purpose of the Study:

  • To develop a novel LSH scheme optimized for the L2 distance.
  • To enhance approximate nearest neighbors search in high-dimensional spaces.
  • To improve the accuracy and/or efficiency of image retrieval systems.

Main Methods:

  • Designed and defined specific hashing functions tailored for the L2 distance.
  • Proved the local-sensitivity property of the proposed hashing functions.
  • Conducted experimental comparisons against Euclidean LSH using real image databases.

Main Results:

  • The new LSH scheme demonstrates superior accuracy in image retrieval compared to the Euclidean scheme at equivalent speeds.
  • Alternatively, it achieves comparable accuracy with significant gains in processing speed.
  • The proposed method is effective for approximate nearest neighbors search in high-dimensional spaces.

Conclusions:

  • The developed LSH scheme adapted to L2 distance is highly relevant for approximate nearest neighbors search.
  • It offers a valuable alternative to Euclidean LSH, especially for applications like image retrieval.
  • The scheme provides flexibility in balancing accuracy and processing speed.