#### Filter Results:

- Full text PDF available (7)

#### Publication Year

2003

2016

- This year (0)
- Last 5 years (4)
- Last 10 years (6)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Jong-Hoon Ahn, Jong-Hoon Oh
- Neural Computation
- 2003

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)

- Jong-Hoon Ahn, Seungjin Choi, Jong-Hoon Oh
- ICML
- 2004

We present a generalization of the nonnegative matrix factorization (NMF), where a multilayer generative network with nonnegative weights is used to approximate the observed nonnegative data. The multilayer generative network with nonnegativity constraints, is learned by a multiplicative uppropagation algorithm, where the weights in each layer are updated… (More)

- Jong-Hoon Ahn, Jong-Hoon Oh, Seungjin Choi
- Neurocomputing
- 2007

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)

- Seungjin Choi, Jong-Hoon Ahn, Andrzej Cichocki
- Neural Processing Letters
- 2006

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… (More)

- Jong-Hoon Ahn
- ICPR
- 2014

We propose an extension of nonnegative matrix factorization (NMF) to multilayer network model for dynamic myocardial 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)

- Jong-Hoon Ahn, Seungjin Choi, Jong-Hoon Oh
- 2004 IEEE International Conference on Acoustics…
- 2004

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)

- Dohyun Kim, Jong-Hoon Ahn, Jongshill Lee, Hoon Ki Park, In Young Kim
- J. Applied Mathematics
- 2012

We propose a new mathematical framework for estimating pulse arrival time PAT . Existing methods of estimating PAT rely on local characteristic points or global parametric models: local characteristic point methods detect points such as foot points, max points, or max slope points, while global parametric methods fit a parametric form to the anacrotic phase… (More)

- Jong-Hoon Ahn
- IEEE Transactions on Image Processing
- 2016

We propose a method for compressive sensing and recovery of binary images. To achieve this, we combine two ideas: in the sensing step, ordered aperture patterns are employed instead of random aperture patterns, and in the recovery step, a dense reconstruction scheme replaces sparse reconstruction. We demonstrate that this approach is more effective for… (More)

- Jong-Hoon Ahn, In Young Kim
- Neural Computation
- 2012

Many areas of science and engineering rely on functional data and their numerical analysis. The need to analyze time-varying functional data raises the general problem of interpolation, that is, how to learn a smooth time evolution from a finite number of observations. Here, we introduce optimal functional interpolation (OFI), a numerical algorithm that… (More)