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Shrinkage estimation for functional principal component scores, with application to the population kinetics of plasma folate
 Biometrics
, 2003
"... We present the application of a nonparametric method to perform functional principal components analysis for functional curve data that consist of measurements of a random trajectory for a sample of subjects. This design typically consists of an irregular grid of time points on which repeated measur ..."
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Cited by 36 (14 self)
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measurements are taken for a number of subjects. We introduce shrinkage estimates for the functional principal component scores that serve as the random effects in the model. Scatterplot smoothing methods are used to estimate the mean function and covariance surface of this model. We propose improved
Probabilistic Principal Component Analysis
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1999
"... Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximumlikelihood estimation of paramet ..."
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Cited by 698 (5 self)
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Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximumlikelihood estimation
Nonlinear component analysis as a kernel eigenvalue problem

, 1996
"... We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all ..."
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Cited by 1549 (83 self)
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We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all
The "Independent Components" of Natural Scenes are Edge Filters
, 1997
"... It has previously been suggested that neurons with line and edge selectivities found in primary visual cortex of cats and monkeys form a sparse, distributed representation of natural scenes, and it has been reasoned that such responses should emerge from an unsupervised learning algorithm that attem ..."
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Cited by 611 (29 self)
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distributions. We compare the resulting ICA filters and their associated basis functions, with other decorrelating filters produced by Principal Components Analysis (PCA) and zerophase whitening filters (ZCA). The ICA filters have more sparsely distributed (kurtotic) outputs on natural scenes. They also
The Alignment Template Approach to Statistical Machine Translation
, 2004
"... A phrasebased statistical machine translation approach — the alignment template approach — is described. This translation approach allows for general manytomany relations between words. Thereby, the context of words is taken into account in the translation model, and local changes in word order f ..."
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Cited by 478 (26 self)
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in detail the process for learning phrasal translations, the feature functions used, and the search algorithm. The evaluation of this approach is performed on three different tasks. For the German–English speech Verbmobil task, we analyze the effect of various system components. On the French
Kernel principal component analysis
 ADVANCES IN KERNEL METHODS  SUPPORT VECTOR LEARNING
, 1999
"... 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 ..."
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Cited by 268 (7 self)
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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
Generalized principal component analysis (GPCA)
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2003
"... This paper presents an algebrogeometric solution to the problem of segmenting an unknown number of subspaces of unknown and varying dimensions from sample data points. We represent the subspaces with a set of homogeneous polynomials whose degree is the number of subspaces and whose derivatives at a ..."
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Cited by 202 (35 self)
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the data set by minimizing certain distance function, thus dealing automatically with moderate noise in the data. A basis for the complement of each subspace is then recovered by applying standard PCA to the collection of derivatives (normal vectors). Extensions of GPCA that deal with data in a
Fast and reliable prediction of noncoding RNAs
 Proc Natl Acad Sci USA
"... We report an efficient method to detect functional RNAs. The approach, which combines comparative sequence analysis and structure prediction, yields excellent results already for a small number of aligned sequences and is suitable for large scalegenomic screens. It consists of two basic components: ..."
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Cited by 332 (45 self)
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We report an efficient method to detect functional RNAs. The approach, which combines comparative sequence analysis and structure prediction, yields excellent results already for a small number of aligned sequences and is suitable for large scalegenomic screens. It consists of two basic components
Functional Principal Components Analysis of Spatially Correlated Data
, 2014
"... This paper focuses on the analysis of spatially correlated functional data. The betweencurve correlation is modeled by correlating functional principal component scores of the functional data. We propose a Spatial Principal Analysis by Conditional Expectation framework to explicitly estimate spat ..."
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This paper focuses on the analysis of spatially correlated functional data. The betweencurve correlation is modeled by correlating functional principal component scores of the functional data. We propose a Spatial Principal Analysis by Conditional Expectation framework to explicitly estimate spa
FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS FOR LONGITUDINAL AND SURVIVAL DATA
"... Abstract: This paper proposes a nonparametric approach for jointly modelling longitudinal and survival data using functional principal components. The proposed model is dataadaptive in the sense that it does not require prespecified functional forms for longitudinal trajectories and it automatic ..."
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Cited by 1 (0 self)
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ducing the dimension of random coefficients, i.e., functional principal component scores. The reduction of dimension achieved from eigendecomposition also makes the model particularly applicable for the sparse data often encountered in longitudinal studies. An iterative selection procedure based on the Akaike
Results 1  10
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1,110,978