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

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...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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.
Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
Linearization and Approximation01:26

Linearization and Approximation

Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
Vector or Cross Product01:17

Vector or Cross Product

Vector multiplication of two vectors yields a vector product, with the magnitude equal to the product of the individual vectors multiplied by the sine of the angle between both the vectors and the direction perpendicular to both the individual vectors. As there are always two directions perpendicular to a given plane, one on each side, the direction of the vector product is governed by the right-hand thumb rule.
Consider the cross product of two vectors. Imagine rotating the first vector about...

You might also read

Related Articles

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

Sort by
Same author

A Cullin1-based SCF E3 ubiquitin ligase targets the InR/PI3K/TOR pathway to regulate neuronal pruning.

PLoS biology·2013
Same author

Recommendations for the management of septic arthritis after ACL reconstruction.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA·2013
Same author

Poly(ADP-ribose) polymerase 1 promotes oxidative-stress-induced liver cell death via suppressing farnesoid X receptor α.

Molecular and cellular biology·2013
Same author

[Clinical significance of changes in T wave and ST segment amplitudes on electrocardiogram from supine to standing position among children with unexplained chest tightness or pain in resting stage].

Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics·2013
Same author

Biomimetic synthesis of equisetin and (+)-fusarisetin A.

Chemistry (Weinheim an der Bergstrasse, Germany)·2013
Same author

ERG Protein Expression Is of Limited Prognostic Value in Men with Localized Prostate Cancer.

ISRN urology·2013
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: May 26, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

Approximate nearest neighbor search by residual vector quantization.

Yongjian Chen1, Tao Guan, Cheng Wang

  • 1Digital Engineering & Simulation Research Center, Huazhong University of Science and Technology, Wuhan 430074, China. chyojn@gmail.com

Sensors (Basel, Switzerland)
|December 14, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces residual vector quantization for efficient approximate nearest neighbor search on unstructured data. The new method offers superior search quality and memory efficiency compared to existing techniques.

Keywords:
approximate nearest neighbor searchhigh-dimensional indexingresidual vector quantization

Related Experiment Videos

Last Updated: May 26, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

Area of Science:

  • Computer Science
  • Machine Learning
  • Data Structures

Background:

  • Product quantization is efficient for large-scale approximate nearest neighbor search but struggles with unstructured vectors.
  • Existing methods lack optimal performance for diverse data types.

Purpose of the Study:

  • To develop novel residual vector quantization approaches for unstructured vectors.
  • To improve the efficiency and accuracy of approximate nearest neighbor search.

Main Methods:

  • Quantizing database vectors using a residual vector quantizer.
  • Approximating Euclidean distance with asymmetric distance calculations.
  • Implementing both exhaustive and non-exhaustive search strategies.

Main Results:

  • The proposed residual vector quantization methods demonstrate effectiveness on unstructured datasets.
  • Achieved superior trade-offs between search quality and memory usage compared to spectral hashing and product quantization.
  • Fast computation of asymmetric distance enables efficient exhaustive search.

Conclusions:

  • Residual vector quantization is a viable and effective approach for approximate nearest neighbor search with unstructured data.
  • The developed methods outperform state-of-the-art techniques in key performance metrics.
  • Offers a promising solution for large-scale similarity search applications.