RPCA

AcronymDefinition
RPCAResponsable du Plan de Continuité d'Activité (French: Head of Business Continuity Plan)
RPCARailroad Passenger Car Alliance
RPCARP Compañia Anonima (Galacia: Joint Stock Company)
RPCARight Posterior Cerebral Artery
RPCAReform Party of California (political party)
RPCArec.pets.cats.anecdotes (newsgroup)
RPCAReverse Passive Cutaneous Anaphylaxis
RPCARelative Power Contribution Analysis (neurophysiology)
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References in periodicals archive ?
where [x.sub.t] and [f.sub.t] are the first two restricted principal components of [x.sub.t] The classical measurement errors, [[epsilon].sub.t], and [[epsilon].sub.t], for the restricted principal components and the model's underlying factors then correct for potential bias in the estimation of the factor loadings by RPCA arising from the presence of weak factors in our panel of time series, as described in the main text.
A source told Dawn that while the formation of district and provincial commissions were facing delay, the establishment of the RPCA was even more complicated as there existed no secretariat to support those bodies at regional level.
The RPCA is widely used to detect moving objects given its robustness against severe disturbances, such as dynamic background and illumination changes.
(i) A novel anomaly detection method of subway passenger flow based on RPCA is proposed, which utilizes low-rank nature of the passenger flow data and the sparsity of anomaly data.
From clustered Gaussian Mixture Model images the blobs were group based on the sparsity obtained in different scales using Group Sparse RPCA. In RPCA the regions with high motion saliency were identified.
A variant of PCA [28], known as a robust PCA (RPCA) [29, 30], is built upon the theory that signals matrix has low-rank structure and the noise is sparsely distributed, affecting only fraction of the signal matrix entries.
Three related methods are compared with the proposed method, including VBM3D [30], RPCA based method [7], and [l.sub.0]-[l.sub.1] based method [8].
[10] focuses on the decomposition of traffic flow matrix and introduced RPCA to accurately decompose the observation traffic flow matrix into submatrices that correspond to different classes of traffic flow.
By carefully evaluating the Rasch principal components analysis (RPCA) results and the person and item reliability and separation findings after each minimal change was made to the instrument, misfitting items and people were eliminated until good model fit and scale brevity were achieved.