Results 31 to 40 of about 4,584 (156)
Remote Sensing, 2019 Dimensionality reduction (DR) is an important preprocessing step in hyperspectral image applications. In this paper, a superpixelwise kernel principal component analysis (SuperKPCA) method for DR that performs kernel principal component analysis (KPCA ...
Lan Zhang, Hongjun Su, Jingwei Shen
doaj +1 more source
Efficient online subspace learning with an indefinite kernel for visual tracking and recognition [PDF]
, 2012 We propose an exact framework for online learning with a family of indefinite (not positive) kernels. As we study the case of nonpositive kernels, we first show how to extend kernel principal component analysis (KPCA) from a reproducing kernel Hilbert ...
Liwicki, Stephan +3 more
core +3 more sources
Indian Journal of Science and Technology, 2016 Objectives: The rapid growth of new vulnerabilities causes the network by Denial of Service attack (DoS). The DoSattack causes traffic flow in network. Therefore it increases the difficulties to detect the DoSattack in traffic by means of misuse detection. The behavior patterns are analyzed in anomaly Anomaly detection to identify the attack.
Lekha Jayabalan, Padmavathi Ganapathi
openaire +1 more source
Revisiting Kernelized Locality-Sensitive Hashing for Improved Large-Scale Image Retrieval [PDF]
, 2014 We present a simple but powerful reinterpretation of kernelized locality-sensitive hashing (KLSH), a general and popular method developed in the vision community for performing approximate nearest-neighbor searches in an arbitrary reproducing kernel ...
Jiang, Ke, Kulis, Brian, Que, Qichao
core +1 more source
International Journal of Computational Intelligence Systems, 2021 Diagnosing incipient faults of rotating machines is very important for reducing economic losses and avoiding accidents caused by faults. However, diagnoses of locations and sizes of incipient faults are very difficult in a noisy background. In this paper,
Heli Wang +3 more
doaj +1 more source
KPCA Based Novelty Detection Method Using Maximum Correntropy Criterion [PDF]
Jisuanji kexue, 2022 Novelty detection is an important research issue in the field of machine learning.Till now,there exist lots of novelty detection approaches.As a commonly used kernel method,kernel principal component analysis(KPCA)has been successfully applied to deal ...
LI Qi-ye, XING Hong-jie
doaj +1 more source
Robust PCA as Bilinear Decomposition with Outlier-Sparsity Regularization [PDF]
, 2011 Principal component analysis (PCA) is widely used for dimensionality reduction, with well-documented merits in various applications involving high-dimensional data, including computer vision, preference measurement, and bioinformatics.
Giannakis, Georgios B., Mateos, Gonzalo
core +1 more source
Modeling mortality with Kernel Principal Component Analysis (KPCA) method
Annals of Actuarial ScienceAbstractAs the global population continues to age, effective management of longevity risk becomes increasingly critical for various stakeholders. Accurate mortality forecasting serves as a cornerstone for addressing this challenge. This study proposes to leverage Kernel Principal Component Analysis (KPCA) to enhance mortality rate predictions.
Yuanqi Wu +4 more
openaire +2 more sources
Clustering via kernel decomposition [PDF]
, 2006 Spectral clustering methods were proposed recently which rely on the eigenvalue decomposition of an affinity matrix. In this letter, the affinity matrix is created from the elements of a nonparametric density estimator and then decomposed to obtain ...
Girolami, M. +2 more
core +2 more sources
Age Sensitivity of Face Recognition Algorithms [PDF]
, 2013 This paper investigates the performance degradation of facial recognition systems due to the influence of age. A comparative analysis of verification performance is conducted for four subspace projection techniques combined with four different distance ...
Deravi, Farzin +2 more
core +1 more source