搜索结果: 1-15 共查到“理论统计学 Clustering”相关记录25条 . 查询时间(0.1 秒)
Adapting the Interrelated Two-way Clustering method for Quantitative Structure-Activity Relationship (QSAR) Modeling of a Diverse Set of Chemical Compounds
Mutagenicity topological indices atom pairs Interrelated Two-way Clustering ridge regression quantum chemical descriptors
2013/6/14
Interrelated Two-way Clustering (ITC) is an unsupervised clustering method developed to divide samples into two groups in gene expression data obtained through microarrays, selecting important genes s...
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
Dynamic Clustering Asymptotics Dependent Dirichlet Process Mixture
2013/6/17
This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolving clusters. The alg...
Statistical Significance of Clustering using Soft Thresholding
Covariance Estimation High Dimension Invariance Principles Unsupervised Learning
2013/6/14
Clustering methods have led to a number of important discoveries in bioinformatics and beyond. A major challenge in their use is determining which clusters represent important underlying structure, as...
Quantum Annealing for Dirichlet Process Mixture Models with Applications to Network Clustering
Quantum annealing Dirichlet process Stochastic optimization Maximum a posteriori estimation Bayesian nonparametrics
2013/6/17
We developed a new quantum annealing (QA) algorithm for Dirichlet process mixture (DPM) models based on the Chinese restaurant process (CRP). QA is a parallelized extension of simulated annealing (SA)...
Variable Selection for Clustering and Classification
Classication Cluster analysis High-dimensional data Mixture models Model-based clus-tering Variable selection
2013/4/28
As data sets continue to grow in size and complexity, effective and efficient techniques are needed to target important features in the variable space. Many of the variable selection techniques that a...
Greedy Feature Selection for Subspace Clustering
Subspace clustering unions of subspaces hybrid linear models sparse ap-proximation structured sparsity nearest neighbors low-rank approximation
2013/5/2
Unions of subspaces are powerful nonlinear signal models for collections of high-dimensional data. However, existing methods that exploit this structure require that the subspaces the signals of inter...
Subspace Clustering via Thresholding and Spectral Clustering
Subspace Clustering Thresholding Spectral Clustering
2013/5/2
We consider the problem of clustering a set of high-dimensional data points into sets of low-dimensional linear subspaces. The number of subspaces, their dimensions, and their orientations are unknown...
Model selection and clustering in stochastic block models with the exact integrated complete data likelihood
Random graphs stochastic block models integrated classication likelihood
2013/4/27
The stochastic block model (SBM) is a mixture model used for the clustering of nodes in networks. It has now been employed for more than a decade to analyze very different types of networks in many sc...
Epidemic diffusion on a graph is a dynamic process that transitions simultaneously to all of a node's neighbors, in contrast to a random walk, which selects only a single neighbor. Epidemic diffusion ...
Methods of Hierarchical Clustering
Hierarchical Clustering hierarchical grid-based algorithm hierarchical density-based approaches
2011/6/20
We survey agglomerative hierarchical clustering algorithms and dis-
cuss efficient implementations that are available in R and other software
environments. We look at hierarchical self-organizing ma...
Multiway Spectral Clustering: A Margin-Based Perspective
Spectral clustering spectral relaxation graph partitioning reproducing kernel Hilbert space large-margin classifi ca-tion Gaussian intrinsic autoregression
2011/3/22
Spectral clustering is a broad class of clustering procedures in which an intractable combinatorial optimization formulation of clustering is "relaxed" into a tractable eigenvector problem, and in whi...
Active Clustering: Robust and Efficient Hierarchical Clustering using Adaptively Selected Similarities
Active Clustering Robust and Efficient Hierarchical Clustering Adaptively Selected Similarities
2011/3/25
Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications. However, in many problems it may be expensive to obtain or compute similaritie...
How the result of graph clustering methods depends on the construction of the graph
graph clustering construction
2011/3/21
We study the scenario of graph-based clustering algorithms such as spectral clustering. Given a set of data points, one first has to construct a graph on the data points and then apply a graph cluste...
Non-Gaussian gravitational clustering field statistics
Cosmology and Extragalactic Astrophysics (astro-ph.CO) Statistics Theory (math.ST)
2010/12/17
In this work we investigate the multivariate statistical description of the matter distribution in the nonlinear regime. We introduce the multivariate Edgeworth expansion of the lognormal distribution...
An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA
Learning (cs.LG) Optimization and Control (math.OC) Machine Learning (stat.ML)
2010/12/17
Many problems in machine learning and statistics can be formulated as (generalized) eigenproblems. In terms of the associated optimization problem, computing linear eigenvectors amounts to finding cri...