Nonlinear principal component analysis using autoassociative neural networks

AIChE Journal

Volumn 37, Issue 2, 1991, Pages 233-243

  Kramer, Mark A ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA PROCESSING - DATA REDUCTION AND ANALYSIS; NEURAL NETWORKS;

AUTOASSOCIATIVE NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS;

CHEMICAL REACTIONS;

EID: 0026113980     PISSN: 00011541     EISSN: 15475905     Source Type: Journal    
DOI: 10.1002/aic.690370209     Document Type: Article

References (32)

1. A Learning Algorithm for Boltzmann Machines
2. Neural Networks and Principal Component Analysis: Learning from Examples without Local Minima
3. Modular Learning in Neural Networks
4. Approximation by Superpositions of a Sigmoidal Function
5. Faster‐Learning Variations on Back‐Propagation: An Empirical Study
6. Application of Karhunen‐Loeve Expansion to Feature Selection and Ordering
7. Partial Least‐Squares Regression: a Tutorial
8. Artificial Neural Network Models of Knowledge Representation in Chemical Engineering
9. A Simple Procedure for Pruning Back‐Propagation Trained Neural Networks
10. Creating Local and Distributed Bottlenecks in Hidden Layers of Back‐Propagation Networks
11. Improvement of the Backpropagation Algorithm for Training Neural Networks
12. Encoding Input/Output Representations in Connectionist Cognitive Systems
13. Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment
14. A Simplified Neuron Model as a Principal Component Analyzer
15. Learning Internal Representations by Error Propagation
16. Optimal Unsupervised Learning in a Single‐Layer Linear Feedforward Neural Network
17. Mapping Techniques for Exploratory Pattern Analysis
18. A Neural Network Methodology for Process Fault Diagnosis