KernelICA : Kernel Independent Component Analysis
Koesner, C. L., & Nordhausen, K. (2021). KernelICA : Kernel Independent Component Analysis. CRAN - The Comprehensive R Archive Network. https://cran.r-project.org/package=KernelICA
Päivämäärä
2021Tekijänoikeudet
© Authors, 2021
The kernel independent component analysis (kernel ICA) method introduced by Bach and Jordan (2003) . The incomplete Cholesky decomposition used in kernel ICA is provided as separate function.
Julkaisija
CRAN - The Comprehensive R Archive Network
Alkuperäislähde
https://cran.r-project.org/package=KernelICAJulkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/51972842
Metadata
Näytä kaikki kuvailutiedotKokoelmat
- Lähdekoodit [2]
Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Multivariate Independent Component Analysis Identifies Patients in Newborn Screening Equally to Adjusted Reference Ranges
Kouři,l Štěpán; de Sousa, Julie; Fačevicová Kamila; Gardlo, Alžběta; Muehlmann, Christoph; Nordhausen, Klaus; Friedecký, David; Adam, Tomáš (MDPI, 2023)Newborn screening (NBS) of inborn errors of metabolism (IEMs) is based on the reference ranges established on a healthy newborn population using quantile statistics of molar concentrations of biomarkers and their ratios. ... -
Snowball ICA : A Model Order Free Independent Component Analysis Strategy for Functional Magnetic Resonance Imaging Data
Hu, Guoqiang; Waters, Abigail B.; Aslan, Serdar; Frederick, Blaise; Cong, Fengyu; Nickerson, Lisa D. (Frontiers Media, 2020)In independent component analysis (ICA), the selection of model order (i.e., number of components to be extracted) has crucial effects on functional magnetic resonance imaging (fMRI) brain network analysis. Model order ... -
Enhancing Performance of Linked Independent Component Analysis : Investigating the Influence of Subjects and Modalities
Xu, Huashuai; Li, Huanjie; Kärkkäinen, Tommi; Cong, Fengyu (IEEE, 2023)In recent years, neuroimaging studies have increasingly been acquiring multiple modalities of data. The benefit of integrating multiple modalities through fusion lies in its ability to combine the unique strengths of each ... -
Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude-only fMRI data
Zhang, Chao‐Ying; Lin, Qiu‐Hua; Niu, Yan‐Wei; Li, Wei‐Xing; Gong, Xiao‐Feng; Cong, Fengyu; Wang, Yu‐Ping; Calhoun, Vince D. (Wiley, 2023)Brain networks extracted by independent component analysis (ICA) from magnitude-only fMRI data are usually denoised using various amplitude-based thresholds. By contrast, spatial source phase (SSP) or the phase information ... -
Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi‐site MRI data
Hao, Yuxing; Xu, Huashuai; Xia, Mingrui; Yan, Chenwei; Zhang, Yunge; Zhou, Dongyue; Kärkkäinen, Tommi; Nickerson, Lisa D.; Li, Huanjie; Cong, Fengyu (Wiley-Blackwell, 2023)Combining magnetic resonance imaging (MRI) data from multi-site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.