Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance

Honglei Zhang, Moncef Gabbouj

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Abstract

    Principal component analysis (PCA) and linear discriminant analysis (LDA) are the most well-known methods to reduce the dimensionality of feature vectors. However, both methods face challenges when used on multilabel data - each data point may be associated to multiple labels. PCA does not take advantage of label information thus the performance is sacrificed. LDA can exploit class information for multiclass data, but cannot be directly applied to multilabel problems. In this paper, we propose a novel dimensionality reduction method for multilabel data. We first introduce the generalized Hamming distance that measures the distance of two data points in the label space. Then the proposed distance is used in the graph embedding framework for feature dimension reduction. We verified the proposed method using three multilabel benchmark datasets and one large image dataset. The results show that the proposed feature dimensionality reduction method consistently outperforms PCA and other competing methods.
    Original languageEnglish
    Title of host publication2018 25th IEEE International Conference on Image Processing (ICIP)
    PublisherIEEE
    Pages1083-1087
    Number of pages5
    ISBN (Electronic)978-1-4799-7061-2
    ISBN (Print)978-1-4799-7062-9
    DOIs
    Publication statusPublished - Oct 2018
    Publication typeA4 Article in conference proceedings
    EventIEEE International Conference on Image Processing -
    Duration: 1 Jan 1900 → …

    Publication series

    Name
    ISSN (Electronic)2381-8549

    Conference

    ConferenceIEEE International Conference on Image Processing
    Period1/01/00 → …

    Keywords

    • feature extraction
    • graph theory
    • learning (artificial intelligence)
    • pattern classification
    • principal component analysis
    • vectors
    • competing methods
    • feature dimensionality reduction method
    • multilabel benchmark datasets
    • graph embedding framework
    • label space
    • generalized hamming distance
    • multilabel problems
    • multiclass data
    • class information
    • label information
    • PCA
    • data point
    • multilabel data
    • feature vectors
    • linear discriminant analysis
    • Dimensionality reduction
    • Principal component analysis
    • Hamming distance
    • Mutual information
    • Measurement
    • Dogs
    • Linear programming
    • dimensionality reduction
    • graph embedding
    • multilabel

    Publication forum classification

    • Publication forum level 1

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