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Related Experiment Videos

A Fast SVD-Hidden-nodes based Extreme Learning Machine for Large-Scale Data Analytics.

Wan-Yu Deng1, Zuo Bai2, Guang-Bin Huang2

  • 1School of Computer, Xian University of Posts & Telecommunications, Shaanxi, China; School of Computer Engineering, Nanyang Technological University, Singapore.

Neural Networks : the Official Journal of the International Neural Network Society
|February 25, 2016
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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This study introduces a Fast Singular Value Decomposition-Hidden-nodes based Extreme Learning Machine (FSVD-H-ELM) to efficiently handle big dimensional data. The novel approach improves classifier performance by reducing dimensionality while maintaining computational tractability.

Area of Science:

  • Machine Intelligence
  • Data Science
  • Computational Statistics

Background:

  • Big dimensional data presents challenges for classifier performance, known as the 'peaking phenomenon'.
  • Dimensionality reduction is a common preprocessing step for high-dimensional datasets.
  • Existing Extreme Learning Machine (ELM) approaches may struggle with the computational complexity of large datasets.

Purpose of the Study:

  • To propose an efficient Extreme Learning Machine (ELM) approach for analyzing big dimensional data.
  • To address the computational complexity associated with Singular Value Decomposition (SVD) in high-dimensional data analysis.
  • To enhance the generalization performance of classifiers on large-scale datasets.

Main Methods:

  • Embedding Singular Value Decomposition (SVD) based hidden nodes into the classical Extreme Learning Machine (ELM).
Keywords:
Big dataBig dimensional dataExtreme Learning MachineFast approximation methodSingular value decomposition

Related Experiment Videos

  • Developing a fast divide and conquer approximation scheme for SVD to maintain computational tractability.
  • Deriving SVD hidden nodes from multiple random data subsets in the proposed Fast Singular Value Decomposition-Hidden-nodes based Extreme Learning Machine (FSVD-H-ELM).

    • Main Results:

    • The proposed FSVD-H-ELM effectively captures underlying characteristics of big dimensional data.
    • FSVD-H-ELM demonstrates superior generalization performance compared to existing state-of-the-art algorithms.
    • The algorithm achieves significant efficiency gains, making it suitable for high-volume data analysis.

    Conclusions:

    • FSVD-H-ELM offers an effective and efficient solution for dimensionality reduction in big dimensional data.
    • The method overcomes the computational limitations of traditional SVD-based approaches.
    • This approach enhances classifier performance and generalization capabilities on large-scale datasets.