Independent component analysis based on symmetrised scatter matrices
Taskinen, S., Sirkiä, S., & Oja, H. (2007). Independent component analysis based on symmetrised scatter matrices. Comput. Statist. Data Anal., 51(10), 5103-5111. https://doi.org/10.1016/j.csda.2006.07.010
Julkaistu sarjassa
Comput. Statist. Data Anal.Päivämäärä
2007Tekijänoikeudet
© Elsevier. This is a pre-print version of an article whose final and definitive form has been published by Elsevier.
A new method for separating the mixtures of independent sources has been proposed recently in [Oja et al. (2006). Scatter matrices and independent component analysis. Austrian J. Statist., to appear]. This method is based on two scatter matrices with the so-called independence property. The corresponding method is now further examined. Simple simulation studies are used to compare the performance of so-called symmetrised scatter matrices in solving the independence component analysis problem. The results are also compared with the classical FastICA method. Finally, the theory is illustrated by some examples.
Julkaisija
ElsevierISSN Hae Julkaisufoorumista
0167-9473Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/16913485
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Tensor clustering on outer-product of coefficient and component matrices of independent component analysis for reliable functional magnetic resonance imaging data decomposition
Hu, Guoqiang; Zhang, Qing; Waters, Abigail B.; Li, Huanjie; Zhang, Chi; Wu, Jianlin; Cong, Fengyu; Nickerson, Lisa D. (Elsevier BV, 2019)Background. Stability of spatial components is frequently used as a post-hoc selection criteria for choosing the dimensionality of an independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) ... -
Examining stability of independent component analysis based on coefficient and component matrices for voxel-based morphometry of structural magnetic resonance imaging
Zhang, Qing; Hu, Guoqiang; Tian, Lili; Ristaniemi, Tapani; Wang, Huili; Chen, Hongjun; Wu, Jianlin; Cong, Fengyu (Springer Netherlands, 2018)Independent component analysis (ICA) on group-level voxel-based morphometry (VBM) produces the coefficient matrix and the component matrix. The former contains variability among multiple subjects for further statistical ... -
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. ... -
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 ... -
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 ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.