Results 231 to 240 of about 26,356,550 (281)

Ipsilateral inhibitory rTMS restores interhemispheric motor cortex balance and reduces phantom limb pain: an exploratory longitudinal nTMS mapping study. [PDF]

J Neuroeng Rehabil
Altawalbeh G, Ursu E, Colella FE, Goldberg M, Liang R, Schröder A, Ille S, Krieg SM, Zavaglia M, Ganguly A, Negwer C, Meyer B, Wagner A.   +12 more
europepmc   +1 more source

Nonlinear kernel-based high-dimensional inference for set-based genetic association studies. [PDF]

Brief Bioinform
Zhang Z, Yang H, Zhu M, Guo R, Chen F, Dong H, Cui Y, Yang H.   +7 more
europepmc   +1 more source

Camel milk fat-based human milk fat substitute. [PDF]

Front Nutr
Bakry IA, Wei W, Li N, Abouzid M, Wang X, Golshany H, Elbarbary A, Nafea AE, Gohary A.   +8 more
europepmc   +1 more source

Kernel Principal Component Analysis

, 1997
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d-pixel products in images.
Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller   +2 more
openaire   +5 more sources

Robust kernel principal component analysis and classification

Advances in Data Analysis and Classification, 2010
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Michiel Debruyne, Tim Verdonck
openaire   +4 more sources

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Robust kernel principal component analysis with optimal mean

Neural Networks, 2022
The kernel principal component analysis (KPCA) serves as an efficient approach for dimensionality reduction. However, the KPCA method is sensitive to the outliers since the large square errors tend to dominate the loss of KPCA. To strengthen the robustness of KPCA method, we propose a novel robust kernel principal component analysis with optimal mean ...
Pei Li, Wenlin Zhang 0001, Chengjun Lu, Rui Zhang 0017, Xuelong Li 0001   +4 more
openaire   +5 more sources

Iterative kernel principal component analysis for image modeling

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005
In recent years, Kernel Principal Component Analysis (KPCA) has been suggested for various image processing tasks requiring an image model such as, e.g., denoising or compression. The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited number of training examples that can be processed.
Kim, KI, Franz, MO, Scholkopf, B
openaire   +6 more sources

Robust Kernel Principal Component Analysis

, 2018
Kernel Principal Component Analysis (KPCA) is a popular generalization of linear PCA that allows non-linear feature extraction. In KPCA, data in the input space is mapped to higher (usually) dimensional feature space where the data can be linearly modeled.
Minh Hoai Nguyen, Fernando De la Torre
openaire   +3 more sources