摘要: This thesis addresses dimensionality reduction problems in classification for both high-dimensional multivariate and functional data.关键词:Statistics Dimensionality reduction for classification with high-dimensional data UNIVERSITY OF SOUTHERN CALIFORNIA Rand R. Wilcox Gareth M. James Tian T. Siva ...
During the last few years, we have witnessed a revolution of computational and methodological advances which allow statistical inference for high-dimensional data. Such data are now common in areas such as bioinformatics and information technology. The terminology 'high-dimensional data' refers to the...
Multiple principal element or high-entropy materials have recently been studied in the two-dimensional (2D) materials phase space. These promising classes of materials combine the unique behavior of solid-solution and entropy-stabilized systems with high aspect ratios and atomically thin characteristics ...
Many statistical methodologies for high-dimensional data as- sume the population normality. Although a few multivariate normality tests have been proposed, they either suffer from low power or have serious size distortion when the dimension is high. In this work, we propose a novel nonparametric tes...
and target values. Recently non-linear regression models like support vector machine (Vapnik, 1995) (SVM), neural network (Ripley, 1996), and random forest (Breiman, 2001) have become extremely popular in quantifying complex functional relationships between high dimensional features and responses. ...
Conventional Vector Autoregressive (VAR) modelling methods applied to high dimensional neural time series data result in noisy solutions that are dense or have a large number of spurious coefficients. This reduces the speed and accuracy of auxiliary computations downstream and inflates the time required...
FUNCTIONAL DEEP NEURAL NETWORK FOR HIGH-DIMENSIONAL DATA ANALYSIS Various examples of methods and systems are provided related to functional deep neural networks (FDNNs), which can be used for high dimensional data analys... LU Qing,S Zhang,T Hou 被引量: 0发表: 2021年 ...
We present a computational method for extracting simple descriptions of high dimensional data sets in the form of simplicial complexes. Our method, called Mapper, is based on the idea of partial clustering of the data guided by a set of functions defined on the data. The proposed method is no...
Fig. 6: Results for high-dimensional human data. Single-cell CRISPR-based experiments (due to ref.32) were used to illustrate the use of the proposed approaches in a high-dimensional human cell setting. Performance was quantified using causal ROC curves (and AUC) computed with respect to a ...
et al. A rare-variant test for high-dimensional data. Eur J Hum Genet 25, 988–994 (2017). https://doi.org/10.1038/ejhg.2017.90 Download citation Received05 August 2016 Revised17 February 2017 Accepted28 March 2017 Published24 May 2017 Issue DateAugust 2017 DOIhttps://doi.org/10.1038/...