Various examples of methods and systems are provided related to functional deep neural networks (FDNNs), which can be used for high dimensional data analysis. In one example, a FDNN can be trained with a training set of omic data to produce a trained FDNN model. The likelihood of a ...
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 Data Analysis (FDA) became in the last few years a major topic in Statistics. Nowadays, it is interacting with many other fields in Statistics, including for instance High Dimensional Statistics, Big Data Analysis, Model/variables selection, Machine Learning, and many other. This Special...
The high-dimensional data created by high-throughput technologies require visualization tools that reveal data structure and patterns in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structure using an information-geometric distance between data ...
Non-linear regression based on reproducing kernel Hilbert space (RKHS) has recently become very popular in fitting high-dimensional data. The RKHS formulation provides an automatic dimension reduction of the covariates. This is particularly helpful when the number of covariates (p) far exceed the num...
2022, 37(1): 131-146 Recent advances in statistical methodologies in evaluating program for high-dimensional data ZHAN Ming-feng1 CAI Zong-wu2 FANG Ying1,3 LIN Ming1,3,∗ Abstract. The era of big data brings opportunities and challenges to developing new statistical methods and models to ...
Comments on: Augmenting the bootstrap to analyze high dimensional genomic data Authors and Affiliations Machine Learning/Intelligent Data Analysis Group, Technical University of Berlin FR 6–9, Franklinstr. 28–29, 10587, Berlin, Germany Nicole Krämer...
High-Dimensional Probability 作者:Roman Vershynin 出版社:Cambridge University Press 副标题:An Introduction with Applications in Data Science 出版年:2018-9-27 页数:296 定价:USD 69.99 装帧:Hardcover 丛书:Cambridge Series in Statistical and Probabilistic Mathematics...
Genome-wide association studies have facilitated the discovery of thousands of loci for hundreds of phenotypes. However, the issue of missing heritability remains unsolved for most complex traits. Locus discovery could be enhanced with both improved powe
A method and an apparatus are provided to visualize high-dimensional data. The method includes primarily visualizing the high-dimensional data at a dimension lower than the high-dim