搜索结果: 1-15 共查到“High-dimensional data”相关记录20条 . 查询时间(0.133 秒)
PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks
Privacy-preserving computations Predictive analysis Federated learning
2019/8/30
Federated Learning (FL) enables a large number of users to jointly learn a shared machine learning (ML) model, coordinated by a centralized server, where the data is distributed across multiple device...
2018年复杂网络高维数据统计挑战研讨会（Meeting the Statistical Challenges in High Dimensional Data and Complex Networks）
2018年 复杂网络高维数据统计挑战 研讨会
2017/11/24
The program aims at showing the role of modern statistical methods in complex data and serves to support interactions among mathematicians, statisticians, engineers and scientists working in the inter...
USING A LABORATORIAL HYPERSPECTRAL IMAGE FOR THE EVALUATION OF FEATURE REDUCTION METHODS FOR THE CLASSIFICATION OF HIGH DIMENSIONAL DATA
Hyperspectral Sensing Feature Selection Feature Extraction Classification
2015/12/31
Thee rapid advances in hyperspectral sensing technology have made it possible to collect remote sensing data in hundreds of bands. However, the data analysis methods which have been successfully appli...
Hessian Eigenmaps: new locally linear embedding techniques for high-dimensional data
Manifold Learning Locally Linear Embedding
2015/8/21
We describe a method to recover the underlying parametrization of scattered data
(mi) lying on a manifold M embedded in high-dimensional Euclidean space. The method,
Hessian-based Locally Linear Emb...
Measuring Polarization in High-dimensional Data: Method and Application to Congressional Speech
Method and Applicatio Congressional Speech
2015/7/17
Standard measures of segregation or polarization are inappropriate for high-dimensional
data such as Internet browsing histories, item-level purchase data, or text. We develop a
model-based measure ...
How to Solve Classification and Regression Problems on High-Dimensional Data with a Supervised Extension of Slow Feature Analysis
Slow feature analysis feature extraction classifi cation regression pattern recognition training graphs nonlinear dimensionality reduction supervised learning high-dimensional data implicitly supervised image analysis
2015/7/10
Supervised learning from high-dimensional data, e.g., multimedia data, is a challenging task. We propose an extension of slow feature analysis (SFA) for supervised dimensionality reduction called grap...
Identification of Signal, Noise, and Indistinguishable Subsets in High-Dimensional Data Analysis
Two-Level Thresholding Signal detection False positive control False negative control Multiple testing Variable screening
2013/6/13
Motivated by applications in high-dimensional data analysis where strong signals often stand out easily and weak ones may be indistinguishable from the noise, we develop a statistical framework to pro...
Modelling interactions in high-dimensional data with Backtracking
Backtracking interactions Lasso parallel computing path algorithm.
2012/9/17
We study the problem of high-dimensional regression when there may be interacting vari-ables. We introduce a new idea called Backtracking, that can be incorporated into many existing high-dimensional ...
Sparse linear discriminant analysis by thresholding for high dimensional data
Classification high dimensionality misclassification rate nor-mality optimal classification rule sparse estimates
2011/6/20
In many social, economical, biological and medical studies, one
objective is to classify a subject into one of several classes based on
a set of variables observed from the subject. Because the prob...
Deciding the dimension of effective dimension reduction space for functional and high-dimensional data
effective dimension reduction space high-dimensional data
2010/11/18
In this paper, we consider regression models with a Hilbert-space-valued predictor and a scalar response, where the response depends on the predictor only through a finite number of projections. The ...
Intelligent Optimization Methods for High-Dimensional Data Classification for Support Vector Machines
Support Vector Machine (SVM) Genetic Algorithm (GA) Particle Swarm Optimization (PSO) Feature Selection Optimization
2013/1/29
Support vector machine (SVM) is a popular pattern classification method with many application areas. SVM shows its outstanding performance in high-dimensional data classification. In the process of cl...
Optimal properties of centroid-based classifiers for very high-dimensional data
Centroid method classification discrimination distance-basedclassifiers high-dimensional data location differences minimax performance
2010/3/11
We show that scale-adjusted versions of the centroid-based classi-
fier enjoys optimal properties when used to discriminate between two
very high-dimensional populations where the principal differen...
A two-sample test for high-dimensional data with applications to gene-set testing
High dimension gene-set testing large p small n martingale central limit theorem multiple comparison
2010/3/10
We propose a two-sample test for the means of high-dimensional
data when the data dimension is much larger than the sample size.
Hotelling’s classical T 2 test does not work for this “large p, small...
Asymptotic inference for high-dimensional data
Covariance matrix estimation c0 functional genomics highdimensionaldata infinite-dimensional central limit theorem
2010/3/10
In this paper, we study inference for high-dimensional data characterized
by small sample sizes relative to the dimension of the data.
In particular, we provide an infinite-dimensional framework to ...
Estimating Bayesian Networks for High-dimensional Data with Complex Mean Structure
Bayesian networks complex mean structure high-dimensionaldata regulatory networks
2010/3/10
The estimation of Bayesian networks given high-dimensional data sets,
in particular given gene expression data sets, has been the focus of much
recent research. While there are many methods availabl...