High dimensional problems
Multi objective evolutionary fuzzy clustering for high dimensional problems
... for High-Dimensional Problems Alessandro ...to high dimensional data. Two problems are addressed: groups (clusters) number discovery and feature selection without performance ...
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Hypothesis Testing for High-Dimensional Problems
... Keywords: multiple directional hypotheses, false discovery rate, familywise error rate, gene expression, skew-normal distribution 1. Introduction In today’s world, most of the statistical inference problems ...
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Statistical Inference for High Dimensional Problems
... Acknowledgments I owe my deepest gratitude to my thesis advisor, Professor Xihong Lin, for being a very kind, encouraging and supporting mentor. I am really thankful to Xihong for giv- ing me the support and freedom to ...
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Multi-objective evolutionary fuzzy clustering for high-dimensional problems
... achieve high partitioning accuracy results [10], ...creasingly high-dimensional data sets from many application domains have posed unprecedented challenges to clustering techniques, which are a ...
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Pattern Alternating Maximization Algorithm for Missing Data in High-Dimensional Problems
... matrix, as in model 2 and 3, the optimal number of nearest neighbors is varying among the different blocks, and a single parameter K is too restrictive. For example in model 2, a variable from the first block is not ...
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Building efficient fuzzy regression trees for large scale and high dimensional problems
... MapReduce presents an abstraction layer to the developers: It divides the problem into smaller tasks, called Map and Reduce, and executes those tasks in parallel taking care of communication, network bandwidth, disk ...
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Convergence results on greedy algorithms for high-dimensional eigenvalue problems*
... High dimensional problems are encountered in many application fields, among which electronic structure calculations, molecular dynamics, uncertainty quantification, multiscale homogenization, and ...
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Greedy algorithms for high-dimensional non-symmetric linear problems*,**
... coercive high-dimensional problems and the theoretical convergence results proved in this ...non-symmetric problems is doomed to failure and motivates the need for more subtle ...discretized ...
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An Investigation of Factors Influencing Algorithm Selection for High Dimensional Continuous Optimisation Problems
... on high dimensional problems previously observed when all benchmark functions were considered in the last ...of problems, the mean ranking trends for DE appear very similar showing little ...
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Active set strategy for high-dimensional non-convex sparse optimization problems
... data fitting term but keep a convex regularization. The re- cent GIST [7] or SCP approaches [10] slightly differ from the above cited works, as they also propose to majorize the loss function. They both consider a ...
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Sequential Monte Carlo methods for high-dimensional inverse problems: a case study for the Navier-Stokes equations
... for high-dimensional inverse ...low- dimensional applications ([ 13 ]) and their validity has been demonstrated by many theoretical results (see [ 11 ] for an exhaustive ...for high- ...
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Algorithms for high dimensional stabbing problems
... We give algorithms based on linear programming for various hyperplane stabbing problems where the ob- jects are line segments or convex polyhedra.. Computational ge[r] ...
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Discovery of low-dimensional structure in high-dimensional inference problems
... recovery problems considered in the ...most problems, which our general approach can handle easier compared to conventional analysis ...classes: problems with linear and nonlinear ...at ...
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Parallel Evolutionary Algorithms and High Dimensional Optimization Problems
... Manuscript submitted May 28, 2018; accepted July 20, 2018. doi: 10.17706/jcp.13.11 1265-1271 . Abstract: Optimization represents a fundamental part of many fields such as industry whose aim is the result of an ...
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Stochastic Particle Flow for Nonlinear High-Dimensional Filtering Problems
... The outline of the article is as follows. We begin by reviewing the stochastic filtering problem in a sequential Monte Carlo framework in Section 2. We abstract the solution in terms of a general map that could adopt any ...
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Sparse Bayesian hierarchical modeling of high-dimensional clustering problems
... both high-dimensional mean and variance that outperforms shrinkage using a non-DP prior, typically with normal distribution for mean and inverse-Gamma distribution for ...
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Comparison of swarm intelligence algorithms for high dimensional optimization problems
... Abstract High dimensional optimization considers being one of the most challenges that face the algorithms for finding an optimal solution for real-world problems ...These problems have been ...
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Selected Problems for High-Dimensional Data - Quantile and Errors-in-Variables Regressions.
... We show that the temporally dependent signal component has an approximately stationary time series structure, and the trial-by-trial correlations reflect similarities between trials that are weakly connected to the mean ...
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Sampling high-dimensional Gaussian distributions for general linear inverse problems
... inverse problems as well as in some hier- archical or latent Gaussian ...inverse problems related to general (non-convolutive) linear observation models and their solution in a Bayesian framework ...
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Preconditioned Low-Rank Methods for High-Dimensional Elliptic PDE Eigenvalue Problems
... eigenvalue problems in computational quantum chemistry, including DMRG for matrix product states and tensor networks, see [30, 22] and the references ...eigenvalue problems arising in DMRG (also called ...
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