Results 11 to 20 of about 1,150,244 (261)
Procrustes Analysis for High-Dimensional Data. [PDF]
Psychometrika, 2022 Abstract The Procrustes-based perturbation model (Goodall in J R Stat Soc Ser B Methodol 53(2):285–321, 1991) allows minimization of the Frobenius distance between matrices by similarity transformation. However, it suffers from non-identifiability, critical interpretation of the transformed matrices, and inapplicability in high ...
Andreella A, Finos L.
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Proceedings of the VLDB Endowment, 2022 This paper introduces an approach to supporting high-dimensional data cubes at interactive query speeds and moderate storage cost. The approach is based on binary(-domain) data cubes that are judiciously partially materialized; the missing information can be quickly reconstructed using statistical or linear programming techniques.
Sachin Basil John, Christoph Koch 0001
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High-dimensional data clustering [PDF]
Computational Statistics & Data Analysis, 2007 Clustering in high-dimensional spaces is a difficult problem which is recurrent in many domains, for example in image analysis. The difficulty is due to the fact that high-dimensional data usually live in different low-dimensional subspaces hidden in the original space.
Bouveyron, Charles +2 more
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High-Dimensional Data Bootstrap
Annual Review of Statistics and Its Application, 2023 This article reviews recent progress in high-dimensional bootstrap. We first review high-dimensional central limit theorems for distributions of sample mean vectors over the rectangles, bootstrap consistency results in high dimensions, and key techniques used to establish those results. We then review selected applications of high-dimensional bootstrap:
Chernozhukov, Victor +3 more
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On Criticality in High-Dimensional Data [PDF]
Neural Computation, 2014 Data sets with high dimensionality such as natural images, speech, and text have been analyzed with methods from condensed matter physics. Here we compare recent approaches taken to relate the scale invariance of natural images to critical phenomena. We also examine the method of studying high-dimensional data through specific heat curves by applying ...
Saremi, Saeed, Sejnowski, Terrence J
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Anomaly Detection in High-Dimensional Data [PDF]
Journal of Computational and Graphical Statistics, 2020 The HDoutliers algorithm is a powerful unsupervised algorithm for detecting anomalies in high-dimensional data, with a strong theoretical foundation. However, it suffers from some limitations that significantly hinder its performance level, under certain circumstances. In this article, we propose an algorithm that addresses these limitations. We define
Priyanga Dilini Talagala +2 more
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Sparse representations of high dimensional neural data
Scientific Reports, 2022 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.
Sandeep K. Mody, Govindan Rangarajan
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Searching for best lower dimensional visualization angles for high dimensional RNA-Seq data [PDF]
PeerJ, 2018 The accumulation of RNA sequencing (RNA-Seq) gene expression data in recent years has resulted in large and complex data sets of high dimensions. Exploratory analysis, including data mining and visualization, reveals hidden patterns and potential ...
Wanli Zhang, Yanming Di
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High Density Subspace Clustering Algorithm for High Dimensional Data
Journal of Harbin University of Science and Technology, 2020 Highdimensional data have the characteristics of sparsity and vulnerability to dimension disaster, which makes it is difficult to ensure the precision and efficiency of high dimensional data clustering Therefore the method of subspace clustering is ...
WAN Jing +3 more
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Data integration with high dimensionality
Biometrika, 2017 SummaryWe consider situations where the data consist of a number of responses for each individual, which may include a mix of discrete and continuous variables. The data also include a class of predictors, where the same predictor may have different physical measurements across different experiments depending on how the predictor is measured.
Gao, Xin, Carroll, Raymond J.
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