Results 21 to 30 of about 4,092,880 (310)
Features Clustering Around Latent Variables for High Dimensional Data [PDF]
E3S Web of Conferences, 2021 Clustering of variables is the task of grouping similar variables into different groups. It may be useful in several situations such as dimensionality reduction, feature selection, and detect redundancies.
Ghizlane Ez-Zarrad +2 more
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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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Industry-scale application and evaluation of deep learning for drug target prediction
Journal of Cheminformatics, 2020 Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling.
Noé Sturm +18 more
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Discriminant methods for high dimensional data [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2019 The main purpose of discriminant analysis is to enable classification of new observations into one of g classes or populations. Discriminant methods suffer when applied to high dimensional data because the sample covariance matrix is singular.
Poompong Kaewumpai, Samruam Chongcharoen
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High-Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality
Entropy, 2020 High-dimensional data and high-dimensional representations of reality are inherent features of modern Artificial Intelligence systems and applications of machine learning.
Alexander N. Gorban +2 more
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High-Dimensional Separability for One- and Few-Shot Learning
Entropy, 2021 This work is driven by a practical question: corrections of Artificial Intelligence (AI) errors. These corrections should be quick and non-iterative. To solve this problem without modification of a legacy AI system, we propose special ‘external’ devices,
Alexander N. Gorban +4 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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Interpretable Approximation of High-Dimensional Data
SIAM Journal on Mathematics of Data Science, 2021 In this paper we apply the previously introduced approximation method based on the ANOVA (analysis of variance) decomposition and Grouped Transformations to synthetic and real data. The advantage of this method is the interpretability of the approximation, i.e., the ability to rank the importance of the attribute interactions or the variable couplings.
Daniel Potts, Michael Schmischke
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