Jong-Hoon Ahn

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We propose a constrained EM algorithm for principal component analysis (PCA) using a coupled probability model derived from single-standard factor analysis models with isotropic noise structure. The single probabilistic PCA, especially for the case where there is no noise, can find only a vector set that is a linear superposition of principal components and(More)
A common derivation of principal component analysis (PCA) is based on the minimization of the squared-error between centered data and linear model, corresponding to the reconstruction error. In fact, minimizing the squared-error leads to principal subspace analysis where scaled and rotated principal axes of a set of observed data, are estimated. In this(More)
We propose an extension of nonnegative matrix factoriza-tion (NMF) to multilayer network model for dynamic my-ocardial PET image analysis. NMF has been previously applied to the analysis and shown to successfully extract three cardiac components and time-activity curve from the image sequences. Here we apply triple nonnegative-matrix factorization to the(More)
Minimization of the reconstruction error (squared-error) leads to a principal subspace analysis (PSA) which estimates the scaled and rotated principal axes of a set of observed data. In this paper, we introduce a new alternative error, the so called integrated-squared-error, the minimization of which determines the exact principal axes (without rotational(More)
In this paper we introduce a new error measure, integrated reconstruction error (IRE) and show that the minimization of IRE leads to principal eigenvectors (without rotational ambiguity) of the data covariance matrix. Then we present iterative algorithms for the IRE minimization, where we use the projection approximation. The proposed algorithm is referred(More)