Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 9 of 9 for “"Kernel Principal Component Analysis"”.
-
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 …
-
Components and principles of streaming principal components
Principal Component Analysis (PCA) is a fundamental pillar of modern data pipelines, but its traditional implementation is woefully inadequate for modern data problems. In this work we present our contributions to the field of streaming principal component analysis---research that adds critical …
-
A Bayesian Fusion Approach and Its Application to Integrating Audio and Visual Signals in HCI
Finally, kernel canonical correlation analysis (CCA) is developed to model nonlinear or high-order correlations between signals from two sources. Kernel CCA uses kernel principal component analysis (PCA), which elegantly combines a nonlinear transformation and linear PCA into a one-step …
-
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) …
-
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 …
-
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 …
-
Clustering and dimensionality reduction for time-series service monitoring data
… The approach combines Deep AutoEncoder with Kernel Principal Component Analysis to produce better data, and then reduce the feature space respectively. Due to the massive size of the dataset, we divide it into six weekly sub-datasets. We show that no vital information is lost for the reduced …
-
Transformation knowledge in pattern analysis with kernel methods - distance and integration kernels
Modern techniques for data analysis and machine learning are so called kernel methods. The most famous and successful one is represented by the support vector machine (SVM) for classification or regression tasks. Further examples are kernel principal component analysis for feature extraction or …
-
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 …