Results 81 to 90 of about 29,752 (189)

A New Feature Extraction Method Based on the Information Fusion of Entropy Matrix and Covariance Matrix and Its Application in Face Recognition

Entropy, 2015
The classic principal components analysis (PCA), kernel PCA (KPCA) and linear discriminant analysis (LDA) feature extraction methods evaluate the importance of components according to their covariance contribution, not considering the entropy ...
Shunfang Wang, Ping Liu
doaj   +1 more source

Kernel PCA with doubly nonlinear mapping for face recognition

, 2005
In this paper, a novel Gabor-based kernel principal component analysis (PCA) with doubly nonlinear mapping is proposed for human face recognition. In our approach, the Gabor wavelets are used to extract facial features, then a doubly nonlinear mapping ...
Xie, XD, Kin-Man Lam, Lam, KM, Xudong Xie   +3 more
core   +1 more source

Kernel PCA per a l’anàlisi de dades òmiques [PDF]

, 2015
Les funcions kernel, per les seves propietats, permeten combinar diferents tipus de dades, essent una possible via per a la integració de dades òmiques.
Planell Picola, Núria
core   +1 more source

Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Normal Controls With Subnetwork Selection and Graph Kernel Principal Component Analysis Based on Minimum Spanning Tree Brain Functional Network

Frontiers in Computational Neuroscience, 2018
Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its early stage (mild cognitive impairment, MCI), has attracted more and more attention recently.
Xiaohong Cui, Jie Xiang, Hao Guo, Guimei Yin, Guimei Yin, Huijun Zhang, Huijun Zhang, Fangpeng Lan, Junjie Chen   +8 more
doaj   +1 more source

Predicting pairwise relations with neural similarity encoders [PDF]

Bulletin of the Polish Academy of Sciences: Technical Sciences, 2018
F. Horn, K.-R. Müller
doaj   +1 more source

Generative Kernel PCA. [PDF]

, 2018
Schreurs, Joachim, Suykens, Johan
openaire   +2 more sources

A New Framework for Multiline Analysis Combined Kernel Principal Component Analysis and Kernel Shapley Additive Explanations: A Case of NGC 1068 ALMA Band 3 Data

The Astronomical Journal
We present a new framework for multiline analysis that combines kernel principal component analysis (Kernel PCA), an unsupervised machine learning method, and Kernel Shapley Additive Explanations (SHAP), an explainable artificial intelligence technique ...
Hiroma Okubo, Tsutomu T. Takeuchi, Shotaro Akaho, Toshiki Saito, Yasuhiko Igarashi, Nario Kuno, Nanase Harada, Akio Taniguchi, Shuro Takano, Taku Nakajima   +9 more
doaj   +1 more source

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

, 2010
A good object representation or object descriptor is one of\ud the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion.
Ross Hayward, Li, Zhengrong, Hayward, Ross F., Zhengrong Li, Rodney Walker, Walker, Rodney A., Yuee Liu, Liu, Yuee   +7 more
core   +1 more source

Eigenvoice speaker adaptation via composite kernel PCA

, 2003
Eigenvoice speaker adaptation has been shown to be effective when only a small amount of adaptation data is available. At the heart of the method is principal component analysis (PCA) employed to find the most important eigenvoices.
Brian Mak, James T. Kwok, Simon Ho
core  

Corrosion Detection in PSC Bridge Tendons Using Kernel PCA Denoising of Measured MFL Signals. [PDF]

Sensors (Basel), 2020
Oh CK, Joh C, Lee JW, Park KY.
europepmc   +1 more source