• t-SNE

  • Referenced in 177 articles [sw22300]
  • high-dimensional data by giving each datapoint a location in a two or three-dimensional ... reveals structure at many different scales. This is particularly important for high-dimensional data that ... seen from multiple viewpoints. For visualizing the structure of very large data sets, we show...
  • Isomap

  • Referenced in 11 articles [sw31686]
  • Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar ... reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. The human brain ... 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...
  • factorcpt

  • Referenced in 12 articles [sw18260]
  • high-dimensional time series factor models with multiple change-points in their second-order structure ... detection in the second-order structure of a high-dimensional time series, into the (relatively ... point detection in the means of high-dimensional panel data. Our methodology circumvents the difficult ... piecewise stationary evolution of the factor structure over time. Our methodology is implemented...
  • hdm

  • Referenced in 7 articles [sw21313]
  • High-Dimensional Metrics. Implementation of selected high-dimensional statistical and econometric methods for estimation ... dimensional causal/ structural parameters are provided which appear in high-dimensional approximately sparse models. Including...
  • GAP

  • Referenced in 19 articles [sw26294]
  • high-dimensional data sets. It provides direct visual perception for exploring structures of a given...
  • onlineCOV

  • Referenced in 1 article [sw42773]
  • Online Change Point Detection in High-Dimensional Covariance Structure. Implement a new stopping rule ... detect anomaly in the covariance structure of high-dimensional online data. The detection procedure ... Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks...
  • LOBPCG

  • Referenced in 33 articles [sw09638]
  • Preconditioned low-rank methods for high-dimensional elliptic PDE eigenvalue problems. We consider elliptic ... eigenvalue problem Ax=λx exhibits Kronecker product structure. In particular, we are concerned with...
  • CorBin

  • Referenced in 2 articles [sw34897]
  • package CorBin: Generate High-Dimensional Binary Data with Correlation Structures. We design algorithms with linear ... studied correlation structures, including exchangeable, decaying-product and K-dependent correlation structures, and extend ... efficient methods to generate high-dimensional binary data with correlation structures.” Submitted...
  • DatabionicSwarm

  • Referenced in 2 articles [sw39852]
  • able to adapt itself to structures of high-dimensional data such as natural clusters characterized ... distance and/or density based structures in the data space. The first module is the parameter ... second module is the parameter-free high-dimensional data visualization technique, which generates projected points ... clustering to data sets with completely different structures drawn from diverse research fields. The comparison...
  • FAMT

  • Referenced in 34 articles [sw11123]
  • FAMT) : simultaneous tests under dependence in high-dimensional data. The method proposed in this package ... dependence on the multiple testing procedures for high-throughput data as proposed by Friguet ... variables is modeled by a factor analysis structure. The number of factors considered...
  • naivereg

  • Referenced in 1 article [sw36604]
  • could face the model uncertainty for structural equation, as large micro dataset is commonly available ... double selection methods are useful for high-dimensional structural equation models. The naivereg is nonparametric...
  • spectralGraphTopology

  • Referenced in 1 article [sw35465]
  • data and hyperconnectivity, learning high-dimensional structures such as graphs from data has become...
  • fabisearch

  • Referenced in 2 articles [sw38226]
  • package fabisearch: Change Point Detection in High-Dimensional Time Series Networks. Implementation of the Factorized ... network (or clustering) structure of multivariate high-dimensional time series. The method is motivated...
  • ROCKET

  • Referenced in 13 articles [sw30016]
  • variables is of fundamental importance in high-dimensional statistics, with numerous applications in biological ... literature exists on methods that estimate the structure of an undirected graphical model, however, little ... inference for edge parameters in a high-dimensional transelliptical model, which generalizes Gaussian and nonparanormal...
  • tlrmvnmvt

  • Referenced in 2 articles [sw41644]
  • rank algorithm for computing relatively high-dimensional multivariate normal (MVN) and Student-t (MVT) probabilities ... Exploiting Low Rank Covariance Structures for Computing High-Dimensional Normal and Student- t Probabilities,” Statistics...
  • DBSCAN

  • Referenced in 4 articles [sw02921]
  • DBSCAN has been mapped to a skeleton-structured program that performs parallel exploration of each ... performance on high-dimensional data, and is general w.r.t. the spatial index structure used...
  • BDgraph

  • Referenced in 19 articles [sw14815]
  • Bayesian structure learning in sparse Gaussian graphical models. Decoding complex relationships among large numbers ... graphical model determination which is a trans-dimensional Markov Chain Monte Carlo (MCMC) approach based ... implement and computationally feasible for high-dimensional graphs. We show our method outperforms alternative Bayesian ... principled and, in practice, sensible approach for structure learning. We illustrate the efficiency...
  • beam

  • Referenced in 1 article [sw42284]
  • marginal and conditional independence structures from high-dimensional data. Leday and Richardson (2019), Biometrics...
  • LargeVis

  • Referenced in 6 articles [sw34905]
  • visualizing large-scale and high-dimensional data in a low-dimensional (typically ... techniques that first compute a similarity structure of the data points and then ... project them into a low-dimensional space with the structure preserved. These two steps suffer ... from scaling to large-scale and high-dimensional data (e.g., millions of data points...
  • ANOVAapprox

  • Referenced in 4 articles [sw40667]
  • ANOVAapprox: Learning multivariate functions with low-dimensional structures using polynomial bases. In this paper ... method for the approximation of high-dimensional functions over finite intervals with respect to complete ... decomposition. For functions with a low-dimensional structure, i.e., a low superposition dimension...