Iterative kernel principal component analysis for image modeling

By Kwang In Kim, Matthias O. Franz, Bernhard Schölkopf. 2005

331 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “Recovering a pre-image is considered necessary in some applications such as image denoising using kernel PCA (Kwok and Tsang 2004, Kim et al. 2005) and visualizing the clustering solutions of a kernel-based clustering algorithm (Dhillon et al. 2004, Jegelka et al. 2009).”
Batch Stationary Distribution Estimation (2020, methodology): “Many existing works have focused on the ﬁnite-dimension setting (Balsubramani et al., 2013; Hardt and Price, 2014; Yang et al., 2017), while Kim et al. (2005) and Xie et al. (2015) have extended the power method to the inﬁnite-dimension case using RKHS.”
Kernel methods in machine learning (2007, neutral): “ractive variants thereof. Kernel PCA has been applied to numerous problems, from preprocessing and invariant feature extraction (Mika et al. [100]) to image denoising and super-resolution (Kim et al. [84]). The basic idea in the latter case is to obtain a set of principal directions in feature space”
Kernel Methods in Computer Vision (2009, unknown): “Schölkopf: “Iterative Kernel Principal Component Analysis for Image Modeling”, PAMI 2005 [Kim et al., 2005] study kernel PCA for the denoising of natural images, in particular face images.”
Online Kernel Principal Component Analysis: A Reduced-Order Model (2012, methodology): “in [17], [18] by kernelizing Oja’s and Sanger’s rules.”
Kernel Methods for Deep Learning (2009, methodology): “, 2012), iterative kernel PCA (Kim et al., 2005), parallelization of support vector machines (Catanzaro et al.”
Online Machine Learning in Big Data Streams (2018, methodology): “Iterative kernel PCA is described in [132, 105] and online kernel PCA in [117].”
Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients (2015, methodology): “These stochastic methods have also been extended to the kernel case [9, 5, 8].”
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior (2010, background): “However, this may introduce blurring and ringing artifacts around major edges as sharp changes are penalized severely.”
The Kernel Least-Mean-Square Algorithm (2008, background): “There have been many studies on kernel-based online learning algorithms such as kernel Hebbian algorithm [7] to solve the kernel PCA, kernel Adaline [8], kernel recursive least squares (RLS) [9], and kernel online learning [1].”