High dimensional data
On the Spectrum of Random Features Maps of High Dimensional Data
... of High Dimensional Data Zhenyu Liao 1 Romain Couillet 1 2 Abstract Random feature maps are ubiquitous in modern statistical machine learning, where they general- ize random projections by means of ...
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ProxiLens: Interactive Exploration of High-Dimensional Data using Projections
... to dimensional- ity reduction leads to distortions in the resulting projection [ Aup07 ...HD data from ...projecting high-dimensional data in a low- dimensional ...
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Class proximity measures—dissimilarity-based classification and display of high-dimensional data
... of high-dimensional ...a high-dimensional biomedical (metabolomic) dataset ( 1 H NMR spectra of a biofluid) several feasible possibilities and processes, based on concepts of the Class ...
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Scalable Collaborative Targeted Learning for High-Dimensional Data
... targeted parameter (the algorithm is described in Section ). The authors show the greedy C-TMLE algorithm exhibits superior relative performance in analyses of sparse data, at the cost of an increase in time ...
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HDclassif: an R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data
... of high-dimensional ...called high dimensional discriminant analysis ...called high dimensional data clustering (HDDC) and uses the expectation-maximization algorithm for ...
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DD-HDS: A method for visualization and exploration of high-dimensional data.
... Mapping high-dimensional data in a low- dimensional space, for example for visualization, is a problem of increasingly major concern in data ...Multi Dimensional Scaling (MDS) ...
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High Dimensional Data Clustering by means of Distributed Dirichlet Process Mixture Models
... a data mining technique intensively used for data analytics, with applications to marketing, security, text/document analysis, or sciences like biology, astronomy, and many ...of high dimen- sional ...
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Kernel methods for high dimensional data analysis
... High Dimensional Data Analysis Since computer power enables massive computations, data analysis has been driven by the necessity to produce algorithms able to recover the structure of ...
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On the visualization of high-dimensional data
... visualizing high dimensional data The kernel PCA is the kernelized version of the PCA, a popular projection ...of high-dimensional data, by selecting the 2 dominant ...
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Relevant Attribute Discovery in High Dimensional Data: Application to Breast Cancer Gene Expressions
... the data objects are described in terms of a large number of ...pipelined data mining approach introduced in [12] us- ing two clustering algorithms in combination with rough sets and extended with genetic ...
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Continual forgetting-free deep learning from high-dimensional data streams
... High-dimensional Data Streams R´ esum´ e : Dans cette th` ese, nous proposons une nouvelle approche de l’apprentissage profond pour la classification des flux de donn´ ees de grande ...
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Contributions to unsupervised learning from massive high-dimensional data streams : structuring, hashing and clustering
... map data onto lower-dimensional representa- ...exist: data-independent and data-dependent hashing ...the data-independent ...c-lower dimensional space followed by a scalar ...
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CrossCat: A fully Bayesian nonparametric method for analyzing heterogeneous, high dimensional data
... analyze high-dimensional datasets with- out imposing restrictive or opaque modeling ...domain-general data analysis method called ...the data, each consisting of a subset of the variables, and ...
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Gaussian mixture models for the classification of high-dimensional vibrational spectroscopy data
... of high-dimensional data classification is to model and classify the data in low-dimensional class specific ...for high-dimensional data and their associated ...
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High-dimensional $p$-norms
... d go to infinity on kXk p has surprising consequences, which may dramatically affect high-dimensional data processing. This effect is usually referred to as the distance concentration phenomenon in ...
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High-dimensional Learning for Extremes
... d . These subsets form a partition of the positive unit sphere S d−1 + . Regarding question (Q1) this partition allows to deal with high-dimensional data. Indeed for β ∈ P d ∗ with cardinality b, the ...
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Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder
... to high-dimensional data [11], ...troscopy data [30], dermoscopy [31] or mammography [32], [33] and demonstrated promising ...small dimensional data, whereas in everyday medical ...
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Similarity Learning for High-Dimensional Sparse Data
... the data dimen- sion) to account for correlation between pairs of fea- ...For high-dimensional data (say, d > 10 4 ), this is problematic for at least three reasons: (i) training the metric ...
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Comparison of Variable Selection Methods for Time-to-Event Data in High-Dimensional Settings
... low-dimensional data, the reference method to study associations with time-to-event endpoints is the Cox propor- tional hazards ...of high-dimensional data (number of covariates > ...
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Automated high-dimensional flow cytometric data analysis
... resulting high-dimensional data and have led to growing interest and recent activity in the development of new computa- tional tools and approaches ...
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