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 Experiment Videos

Supervised nonlinear dimensionality reduction for visualization and classification.

Xin Geng1, De-Chuan Zhan, Zhi-Hua Zhou

  • 1National Laboratory for Novel Software Technology, Nanjing University, China. gengx@lamda.nju.edu.cn

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|December 22, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

MAPLE: interpretable deep learning identifies selective antimicrobial peptides using joint evolutionary-physicochemical analysis.

Briefings in bioinformatics·2026
Same author

Stochastic Approximation Approaches to Group Distributionally Robust Optimization and Beyond.

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

Transcriptomic and proteomic insights into the molecular mechanism underlying cell wall biosynthesis in Moso bamboo (Phyllostachys edulis).

Plant cell reports·2026
Same author

Selecting and Distilling Cross-Label Models.

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

PAGD: the Persea americana Genome Database and a Docker-based transcriptome analysis workflow.

BMC genomics·2026
Same author

Causal effects of plasma protein-protein ratios on lung cancer subtypes: A proteome-wide Mendelian randomization and mediation analysis.

Medicine·2026
Same journal

Strategic Ability Updating in Concurrent Games by Coalitional Commitment.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2015
Same journal

Meta-Analysis of the First Facial Expression Recognition Challenge.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Adjustable model-based fusion method for multispectral and panchromatic images.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Face Feature Weighted Fusion Based on Fuzzy Membership Degree for Video Face Recognition.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

A New Adaptive Fast Cellular Automaton Neighborhood Detection and Rule Identification Algorithm.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Human-arm-and-hand-dynamic model with variability analyses for a stylus-based haptic interface.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
See all related articles

Supervised Isomap (S-Isomap) enhances nonlinear dimensionality reduction by integrating class information, improving robustness for data visualization and classification tasks, especially with noisy real-world data.

Area of Science:

  • Machine Learning
  • Data Science
  • Computer Vision

Background:

  • Dimensionality reduction is crucial for visualization and classification.
  • Isomap is a powerful nonlinear technique but sensitive to noise.
  • Real-world data often presents challenges for existing methods.

Purpose of the Study:

  • To propose an improved Isomap algorithm, S-Isomap, for robust nonlinear dimensionality reduction.
  • To leverage class information for supervised dimensionality reduction.
  • To enhance data visualization and classification performance on real-world datasets.

Main Methods:

  • Developed S-Isomap, a supervised nonlinear dimensionality reduction technique.
  • Constructed a neighborhood graph using a novel dissimilarity measure integrating class information.

Related Experiment Videos

  • Evaluated S-Isomap against Isomap, LLE, and WeightedIso for visualization and classification.

    • Main Results:

    • S-Isomap outperformed Isomap, LLE, and WeightedIso in visualization tasks.
    • As a preprocessing step, S-Isomap significantly improved classification accuracy compared to unsupervised methods.
    • S-Isomap demonstrated competitive performance against established classifiers like SVM and K-NN.

    Conclusions:

    • S-Isomap offers a robust approach to nonlinear dimensionality reduction by incorporating supervised information.
    • The proposed dissimilarity measure effectively guides the dimensionality reduction process.
    • S-Isomap shows significant potential for improving both visualization and classification of complex, real-world data.