Global ETD Search
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Showing 1 to 5 of 5 for “"Kernel Principal Component Analysis (kPCA)"”.
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Feature Selection Using Genetic Algorithms for Human Gait Recognition
… accuracy. First, features are extracted using Kernel Principal Component Analysis (KPCA) on four spatio-temporal projections of silhouettes. Then, GAs are applied to choose a subset of Eigen-vectors that represent a subject's identity. Our experimental results, conducted on Georgia Tech (GT) …
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Nonlinear dynamic process monitoring using kernel methods
The application of kernel methods in process monitoring is well established. How- ever, there is need to extend existing techniques using novel implementation strate- gies in order to improve process monitoring performance. For example, process monitoring using kernel principal component analysis …
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Data Reduction in Smart Grid
… in smart grid. The techniques studied are principal component analysis (PCA), isometric feature mapping (ISOMAP), kernel principal component analysis (KPCA), locally linear embedding (LLE), laplacian eigenmaps, t-Distributed stochastic neighbor embedding (t-SNE) and autoenoders. The …
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Modified Kernel Principal Component Analysis and Autoencoder Approaches to Unsupervised Anomaly Detection
… in two existing anomaly detection algorithms, Kernel Principal Component Analysis (KPCA) and Autoencoders (AE), and proposes novel solutions to improve both of their performances in the unsupervised settings. Anomaly detection has several useful applications, such as intrusion detection, fault …
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Building a robust clinical diagnosis support system for childhood cancer using data mining methods
… and processes of cancer requires thorough analysis of many coding and noncoding regions of the genome. Data mining and knowledge discovery have been applied to datasets across many industries, including bioinformatics. However, data mining faces a major challenge in its application to …