• t-SNE

• Referenced in 100 articles [sw22300]
• technique called ”t-SNE” that visualizes high-dimensional data by giving each datapoint a location ... scales. This is particularly important for high-dimensional data that lie on several different...

• ROBPCA

• Referenced in 68 articles [sw11592]
• matrix of the data and hence is highly sensitive to outlying observations. Two robust approaches ... projection pursuit and can handle high-dimensional data. Here we propose the ROBPCA approach, which...

• mboost

• Referenced in 63 articles [sw07331]
• additive and interaction models to potentially high-dimensional data...

• GGobi

• Referenced in 43 articles [sw00345]
• open source visualization program for exploring high-dimensional data. It provides highly dynamic and interactive...

• FAMT

• Referenced in 30 articles [sw11123]
• FAMT) : simultaneous tests under dependence in high-dimensional data. The method proposed in this package...

• ARfit

• Referenced in 38 articles [sw00046]
• efficient, in particular when the data are high-dimensional. ARfit modules construct approximate confidence intervals...

• WGCNA

• Referenced in 21 articles [sw07123]
• perform Weighted Correlation Network Analysis on high-dimensional data. Includes functions for rudimentary data cleaning...

• GAP

• Referenced in 18 articles [sw26294]
• matrix visualization (MV) and clustering of high-dimensional data sets. It provides direct visual perception...

• HDclassif

• Referenced in 15 articles [sw11114]
• data, based on the assumption that high-dimensional data live in different subspaces with...

• SOM_PAK

• Referenced in 23 articles [sw15446]
• codebook vectors into a high-dimensional input data space to approximate to its data sets...

• uniCox

• Referenced in 14 articles [sw19131]
• model.. Especially useful for high-dimensional data, including microarray data...

• Isomap

• Referenced in 11 articles [sw31686]
• Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar ... problem of dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations ... everyday perception, extracting from its high-dimensional sensory inputs-30,000 auditory nerve fibers ... Here we describe an approach to solving dimensionality reduction problems that uses easily measured local...

• SparseFIS

• Referenced in 11 articles [sw13736]
• this paper, we deal with a novel data-driven learning method [sparse fuzzy inference systems ... evaluated, which is based on high-dimensional data from industrial processes and based on benchmark...

• WeightedPortTest

• Referenced in 11 articles [sw12429]
• Statistical Association: We exploit ideas from high-dimensional data analysis to derive new portmanteau tests...

• SpectralCAT

• Referenced in 8 articles [sw18794]
• required for most real-world applications data to handle both feature types and their ... called SpectralCAT, for unsupervised clustering of high-dimensional data that contains numerical or nominal ... suggest to automatically transform the high-dimensional input data into categorical values. This is done...

• SPECTRODE

• Referenced in 9 articles [sw13984]
• behavior of statistical methods under high-dimensional asymptotics. However, the applicability of the framework ... data is a sample from a multivariate distribution with a given covariance matrix. Under high ... dimensional asymptotics, there is a deterministic map from the distribution of eigenvalues of the population ... broaden the use of RMT in high-dimensional data analysis...

• IPSep-CoLa

• Referenced in 9 articles [sw09789]
• utility of our technique with sample data from a number of practical applications including gene ... networks, terrorist networks and visualization of high-dimensional data...

• bartMachine

• Referenced in 9 articles [sw10962]
• detection, model diagnostic plots, incorporation of missing data and the ability to save trees ... handling both large sample sizes and high-dimensional data...

• sva

• Referenced in 6 articles [sw21306]
• identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed ... directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used...

• ZIFA

• Referenced in 9 articles [sw32141]
• gene expression analysis. Single-cell RNA-seq data allows insight into normal cellular function ... cell level. Dimensionality reduction of such high-dimensional data sets is essential for visualization...