Analysis of point-to-point lung motion with full inspiration and expiration CT data using non-linear optimization method: optimal geometric assumption model for the effective registration algorithm

2007 ◽  
Author(s):  
Namkug Kim ◽  
Joon Beom Seo ◽  
Jeong Nam Heo ◽  
Suk-Ho Kang
Keyword(s):  
Linear Optimization ◽  
Optimization Method ◽  
Registration Algorithm ◽  
Full Inspiration ◽  
Lung Motion ◽  
Non Linear ◽  
Geometric Assumption ◽  
Ct Data ◽  
Point To Point

scholarly journals Self-Expressive Kernel Subspace Clustering Algorithm for Categorical Data with Embedded Feature Selection

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1680
Author(s):  
Hui Chen ◽  
Kunpeng Xu ◽  
Lifei Chen ◽  
Qingshan Jiang
Keyword(s):  
Categorical Data ◽  
Clustering Algorithm ◽  
Linear Optimization ◽  
Subspace Clustering ◽  
Optimization Method ◽  
Kernel Clustering ◽  
Non Linear ◽  
Series Of Experiments ◽  
Real World Datasets ◽  
New Feature

Kernel clustering of categorical data is a useful tool to process the separable datasets and has been employed in many disciplines. Despite recent efforts, existing methods for kernel clustering remain a significant challenge due to the assumption of feature independence and equal weights. In this study, we propose a self-expressive kernel subspace clustering algorithm for categorical data (SKSCC) using the self-expressive kernel density estimation (SKDE) scheme, as well as a new feature-weighted non-linear similarity measurement. In the SKSCC algorithm, we propose an effective non-linear optimization method to solve the clustering algorithm’s objective function, which not only considers the relationship between attributes in a non-linear space but also assigns a weight to each attribute in the algorithm to measure the degree of correlation. A series of experiments on some widely used synthetic and real-world datasets demonstrated the better effectiveness and efficiency of the proposed algorithm compared with other state-of-the-art methods, in terms of non-linear relationship exploration among attributes.

Determination of Total Energy Tight Binding Parameters from First Principles Calculations Using Adaptive Simulated Annealing

2001 ◽  
Vol 700 ◽  
Author(s):  
Anders G. Froseth ◽  
Peter Derlet ◽  
Ragnvald Hoier
Keyword(s):  
Simulated Annealing ◽  
Total Energy ◽  
First Principles ◽  
Linear Optimization ◽  
Tight Binding ◽  
Accurate Method ◽  
Optimization Method ◽  
Binding Parameters ◽  
Non Linear

AbstractEmpirical Total Energy Tight Binding (TETB) has proven to be a fast and accurate method for calculating materials properties for various system, including bulk, surface and amorphous structures. The determination of the tight binding parameters from first-principles results is a multivariate, non-linear optimization problem with multiple local minima. Simulated annealing is an optimization method which is flexible and “guaranteed” to find a global minimum, opposed to classical methods like non-linear least squares algorithms. As an example results are presented for a nonorthogonal s,p parameterization for Silicon based on the NRL tight binding formalism.

Estimation of lung motion fields in 4D CT data by variational non-linear intensity-based registration: A comparison and evaluation study

2014 ◽  
Vol 59 (15) ◽  
pp. 4247-4260 ◽  
Author(s):  
René Werner ◽  
Alexander Schmidt-Richberg ◽  
Heinz Handels ◽  
Jan Ehrhardt
Keyword(s):  
Evaluation Study ◽  
4D Ct ◽  
Lung Motion ◽  
Non Linear ◽  
Ct Data ◽  
Linear Intensity
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