Tangent Hyperplane Kernel Principal Component Analysis for Denoising PROJECT TITLE :Tangent Hyperplane Kernel Principal Component Analysis for DenoisingABSTRACT:Kernel principal component analysis (KPCA) is a method widely used for denoising multivariate data. Using geometric arguments, we investigate why a projection operation inherent to all existing KPCA denoising algorithms can sometimes cause very poor denoising. Based on this, we propose a modification to the projection operation that remedies this problem and can be incorporated into any of the existing KPCA algorithms. Using toy examples and real datasets, we show that the proposed algorithm can substantially improve denoising performance and is more robust to misspecification of an important tuning parameter. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Generalized SMO Algorithm for SVM-Based Multitask Learning Modified Kolmogorov's Neural Network in the Identification of Hammerstein and Wiener Systems
