Results 1 to 10 of about 649,574 (263)
Probabilistic Principal Component Analysis [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1999 Summary Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based on a probability model. We demonstrate how the principal axes of a set of observed data vectors may be determined through maximum likelihood estimation of parameters in a latent variable model that is closely ...
Tipping, Michael E. +1 more
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Ensemble Principal Component Analysis
IEEE AccessEfficient representations of data are essential for processing, exploration, and human understanding, and Principal Component Analysis (PCA) is one of the most common dimensionality reduction techniques used for the analysis of large, multivariate ...
Olga Dorabiala +2 more
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New Insight into Development of Environmental - Economic Model Based on a Composite Environmental Quality Index: An Application of Principal Components Analysis [PDF]
تصمیم گیری و تحقیق در عملیات, 2021 Purpose: This paper aimed to create a composite index for environmental quality. Since the results of empirical studies on economic - environmental variables nexus, considering different environmental indicators, are not consistent with each other; it ...
Hossein Ali Fakher +4 more
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Modal Principal Component Analysis [PDF]
Neural Computation, 2020 Principal component analysis (PCA) is a widely used method for data processing, such as for dimension reduction and visualization. Standard PCA is known to be sensitive to outliers, and various robust PCA methods have been proposed. It has been shown that the robustness of many statistical methods can be improved using mode estimation instead of mean ...
Keishi Sando, Hideitsu Hino
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JEDi: java essential dynamics inspector — a molecular trajectory analysis toolkit
BMC Bioinformatics, 2021 Background Principal component analysis (PCA) is commonly applied to the atomic trajectories of biopolymers to extract essential dynamics that describe biologically relevant motions. Although application of PCA is straightforward, specialized software to
Charles C. David +2 more
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Robust Bilinear Probabilistic Principal Component Analysis
Algorithms, 2021 Principal component analysis (PCA) is one of the most popular tools in multivariate exploratory data analysis. Its probabilistic version (PPCA) based on the maximum likelihood procedure provides a probabilistic manner to implement dimension reduction ...
Yaohang Lu, Zhongming Teng
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Online Tensor Robust Principal Component Analysis
IEEE Access, 2022 Online robust principal component analysis (RPCA) algorithms recursively decompose incoming data into low-rank and sparse components. However, they operate on data vectors and cannot directly be applied to higher-order data arrays (e.g. video frames). In
Mohammad M. Salut, David V. Anderson
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A Generalization of Principal Component Analysis [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 Conventional principal component analysis (PCA) finds a principal vector that maximizes the sum of second powers of principal components. We consider a generalized PCA that aims at maximizing the sum of an arbitrary convex function of principal components. We present a gradient ascent algorithm to solve the problem.
Samuele Battaglino, Erdem Koyuncu
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Parameterized principal component analysis [PDF]
Pattern Recognition, 2018 When modeling multivariate data, one might have an extra parameter of contextual information that could be used to treat some observations as more similar to others. For example, images of faces can vary by age, and one would expect the face of a 40 year old to be more similar to the face of a 30 year old than to a baby face.
Ajay Gupta, Adrian Barbu
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Principal Component Analysis Based Wavelet Transform [PDF]
Engineering and Technology Journal, 2012 The principal component analysis (PCA) is a valuable statistical means, implemented in time domain that has found application in many fields such as face recognition and image compression, and is a common technique for finding patterns in data of high ...
Hana M. Salman
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