Results 151 to 160 of about 3,257 (211)

Using machine learning and single nucleotide polymorphisms for improving rheumatoid arthritis risk Prediction in postmenopausal women. [PDF]

PLOS Digit Health
Xu Y, Wu Q.
europepmc   +1 more source

Prediction of river dissolved oxygen (DO) based on multi-source data and various machine learning coupling models. [PDF]

PLoS One
Zhao Y, Chen M.
europepmc   +1 more source

Myeloid activation clears ascites and reveals IL27-dependent regression of metastatic ovarian cancer. [PDF]

J Exp Med
Murphy B, Miyamoto T, Manning BS, Mirji G, Ugolini A, Kannan T, Hamada K, Zhu YP, Claiborne DT, Huang L, Zhang R, Nefedova Y, Kossenkov A, Veglia F, Shinde R, Zhang N.   +15 more
europepmc   +1 more source

KPCA for semantic object extraction in images

Pattern Recognition, 2008
In this paper, we kernelize conventional clustering algorithms from a novel point of view. Based on the fully mathematical proof, we first demonstrate that kernel KMeans (KKMeans) is equivalent to kernel principal component analysis (KPCA) prior to the ...
Dacheng Tao, Xuelong Li
exaly   +4 more sources

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L1 norm based KPCA for novelty detection

Pattern Recognition, 2013
Novelty detection is a one class classification problem, and it builds up the model with only normal samples, based on which the novelty is detected. Though conventional KPCA is an effective method of building one class classification models, it is prone to being affected by the presence of outliers due to its inherent properties of L2 norm.
Huangang Wang, Junwu Zhou, Wenli Xu
exaly   +3 more sources

Generalized KPCA by adaptive rules in feature space

International Journal of Computer Mathematics, 2010
Principal component analysis (PCA) is well recognized in dimensionality reduction, and kernel PCA (KPCA) has also been proposed in statistical data analysis. However, KPCA fails to detect the nonlinear structure of data well when outliers exist. To reduce this problem, this paper presents a novel algorithm, named iterative robust KPCA (IRKPCA).
Yanwei Pang, Yuan Yuan
exaly   +2 more sources

Fall Detection Based on KPCA and 3D KPCA

2016 IEEE Trustcom/BigDataSE/ISPA, 2016
Falls in elderly remain a very important public health care issue. The wearable devices based on tri-axial accelerator proves to be an effective tool for fall detection in the recent years. In this paper, we propose an approach to distinguish falls and normal activities of daily living (ADL).
Hui Wang, Xiaohe Chen, Xinjian Chen 0001, Peilin Zang, Yunqing Liu, Lirong Wang   +5 more
openaire   +1 more source

Image classification with parallel KPCA‐PCA network

Computational Intelligence, 2022
AbstractPrincipal component analysis (PCA) is widely used in computer vision for object detection. In this article, we take advantage of the algorithms of PCA and kernel principal component analysis (KPCA) to construct a deep learning model named parallel KPCA‐PCA network (PK‐PCANet).
Feng Yang, Zheng Ma 0005, Mei Xie
openaire   +1 more source

Adaptive KPCA Modeling of Nonlinear Systems

IEEE Transactions on Signal Processing, 2015
This paper proposes an adaptive algorithm for kernel principal component analysis (KPCA). Compared to existing work: i) the proposed algorithm does not rely on assumptions, ii) combines the up- and downdating step to become a single operation, iii) the adaptation of the eigendecompsition can, computationally, reduce to $O(N)$ and iv) the proposed ...
Zhe Li, Uwe Kruger 0001, Lei Xie 0007, Ali Almansoori, Hongye Su   +4 more
openaire   +2 more sources

Learning KPCA for Face Recognition

2013
Kernel principal component analysis (KPCA) is an effective method for face recognition. However, the expression of its final solution needs to take advantage of all training examples, such that its run in real-world application with large scale training set is time-consuming.
Wangli Hao, Jianwu Li, Xiao Zhang
openaire   +2 more sources