Journal ArticleDOI
A Hybrid Methodology For On-Line Process Monitoring
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TLDR
A hybrid strategy of using locally linear embedding for nonlinear dimensionality reduction of high dimensional data and support vector machines for classification of the resultant features is proposed as a robust methodology for process monitoring.Abstract:
A hybrid strategy of using (i) locally linear embedding for nonlinear dimensionality reduction of high dimensional data and (ii) support vector machines for classification of the resultant features is proposed as a robust methodology for process monitoring. Illustrative examples substantiate the methodology vis-a-vis current practice.read more
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Book ChapterDOI
Fault diagnosis using dynamic time warping
TL;DR: A novel methodology for online fault diagnosis with Dynamic Time Warping has been suggested and its performance has been investigated using two simulated case studies using a warping window constraint and a Lower Bounding measure.
References
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Journal ArticleDOI
Nonlinear dimensionality reduction by locally linear embedding.
Sam T. Roweis,Lawrence K. Saul +1 more
TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
Journal ArticleDOI
A global geometric framework for nonlinear dimensionality reduction.
TL;DR: An approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set and efficiently computes a globally optimal solution, and is guaranteed to converge asymptotically to the true structure.
Journal ArticleDOI
Statistical pattern recognition: a review
TL;DR: The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
Proceedings Article
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
Mikhail Belkin,Partha Niyogi +1 more
TL;DR: The algorithm provides a computationally efficient approach to nonlinear dimensionality reduction that has locality preserving properties and a natural connection to clustering.
Journal ArticleDOI
A Nonlinear Mapping for Data Structure Analysis
TL;DR: An algorithm for the analysis of multivariate data is presented along with some experimental results that is based upon a point mapping of N L-dimensional vectors from the L-space to a lower-dimensional space such that the inherent data "structure" is approximately preserved.