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 Video

Updated: Feb 25, 2026

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

8.1K

LLE Score: A New Filter-Based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its

Chao Yao, Ya-Feng Liu, Bo Jiang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |August 3, 2017
    PubMed
    Summary

    Related Concept Videos

    Upsampling01:22

    Upsampling

    668
    Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
    668

    You might also read

    Related Articles

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

    Sort by
    Same author

    Improved the slow digestion property of maize starch using partially β-amylolysis.

    Food chemistry·2014
    Same author

    Blend-modification of soy protein/lauric acid edible films using polysaccharides.

    Food chemistry·2014
    Same author

    Structure and physicochemical properties of octenyl succinic esters of sugary maize soluble starch and waxy maize starch.

    Food chemistry·2014
    Same author

    [Effects of left renal vein division on postoperative renal function during open repair of abdominal aortic aneurysm].

    Zhonghua yi xue za zhi·2014
    Same author

    Association of four insulin resistance genes with type 2 diabetes mellitus and hypertension in the Chinese Han population.

    Molecular biology reports·2014
    Same author

    Neuroprotective effect of pseudoginsenoside-f11 on a rat model of Parkinson's disease induced by 6-hydroxydopamine.

    Evidence-based complementary and alternative medicine : eCAM·2014

    This study introduces LLE score, a novel feature selection method for unsupervised learning. LLE score effectively identifies representative features by measuring local structure differences, outperforming existing techniques in image classification tasks.

    Area of Science:

    • Machine Learning
    • Data Science
    • Computer Vision

    Background:

    • Feature selection is crucial for high-dimensional data analysis.
    • Unsupervised feature selection is challenging due to the lack of class labels.
    • Existing methods like data variance, Laplacian score, and sparsity score have limitations.

    Purpose of the Study:

    • To propose a new unsupervised feature selection method based on Locally Linear Embedding (LLE).
    • To address the limitations of directly applying LLE to feature selection.
    • To introduce the LLE score criterion for measuring local structure differences.

    Main Methods:

    • Investigated the application of Locally Linear Embedding (LLE) for feature selection.
    • Developed a new filter-based feature selection method named LLE score.

    Related Experiment Videos

    Last Updated: Feb 25, 2026

    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

    8.1K
  • Proposed a criterion that measures the difference between the local structure of each feature and the original data.

    • Main Results:

    • The proposed LLE score method addresses limitations of direct LLE application, such as scaling invariance and graph change capture.
    • Experiments on face, object, and handwriting digit datasets demonstrated superior performance.
    • LLE score outperformed state-of-the-art methods including data variance, Laplacian score, and sparsity score in classification tasks.

    Conclusions:

    • LLE score is an effective and robust method for unsupervised feature selection.
    • The proposed method offers significant improvements over existing techniques for image classification.
    • LLE score provides a valuable tool for analyzing high-dimensional data in unsupervised settings.