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

Modelling multiple-cause structure using rectification constraints

D Charles1, C Fyfe

  • 1Department of Computing and Information Systems, University of Paisley, UK. char-ci0@paisley.ac.uk

Network (Bristol, England)
|December 23, 1998
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

Type of HCBS Support Provided Predictive of Frailty Status Change Among Older Participants.

Journal of the American Medical Directors Association·2021
Same author

The effectiveness and safety of introducing condom-catheter uterine balloon tamponade for postpartum haemorrhage at secondary level hospitals in Uganda, Egypt and Senegal: a stepped wedge, cluster-randomised trial.

BJOG : an international journal of obstetrics and gynaecology·2019
Same author

Dietary carbohydrate rather than protein intake drives colonic microbial fermentation during weight loss.

European journal of nutrition·2018
Same author

Protocol for a randomised controlled trial comparing aqueous with alcoholic chlorhexidine antisepsis for the prevention of superficial surgical site infection after minor surgery in general practice: the AVALANCHE trial.

BMJ open·2016
Same author

A randomized study of very low-dose factor VIII prophylaxis in severe haemophilia - A success story from a resource limited country.

Haemophilia : the official journal of the World Federation of Hemophilia·2016
Same author

Severe cutaneous vasculitis: an unusual complication of equine antithymocyte globulin treatment.

QJM : monthly journal of the Association of Physicians·2015
Same journal

Enhancing IoT security: A Creative Swagger Optimization algorithm for DDoS defence.

Network (Bristol, England)·2026
Same journal

Parametric optimization for electrical discharge diamond grinding (EDDG) system using dual approach.

Network (Bristol, England)·2025
Same journal

A novel lung cancer diagnosis model using hybrid convolution (2D/3D)-based adaptive DenseUnet with attention mechanism.

Network (Bristol, England)·2025
Same journal

Hybrid optimization enabled Eff-FDMNet for Parkinson's disease detection and classification in federated learning.

Network (Bristol, England)·2025
Same journal

AI-driven plant disease detection with tailored convolutional neural network.

Network (Bristol, England)·2025
Same journal

Layer modified residual Unet++ for speech enhancement using Aquila Black widow optimizer algorithm.

Network (Bristol, England)·2025
See all related articles

This study introduces an unsupervised artificial neural network that self-organizes to reveal hidden data causes. An exponential function proved most effective for robustly identifying underlying data structures, even with noisy inputs.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Unsupervised learning aims to discover patterns in data without predefined labels.
  • Sparse distributed representations are efficient ways to encode information.
  • Principal Component Analysis (PCA) is a common dimensionality reduction technique.

Purpose of the Study:

  • To develop an artificial neural network capable of unsupervised self-organization.
  • To create a sparse distributed representation of underlying data causes.
  • To evaluate the impact of rectification constraints on network performance.

Main Methods:

  • An artificial neural network architecture was designed.
  • Rectification constraints were introduced to a PCA network.

Related Experiment Videos

  • Experiments were conducted to compare different rectification methods (e.g., exponential function).
  • Input data preprocessing (unit variance) was explored.

    • Main Results:

    • The network successfully self-organized to form sparse distributed representations.
    • An exponential function applied to the network's output demonstrated superior reliability in identifying all data causes.
    • This method proved effective even with highly noisy input data.
    • Input preprocessing to unit variance enhanced cause discovery with variable power inputs.

    Conclusions:

    • The proposed artificial neural network offers a robust method for unsupervised discovery of underlying data causes.
    • The exponential rectification constraint is particularly effective for noisy datasets.
    • Network methodologies are straightforward and demonstrate high reliability across multiple trials.