Results 11 to 20 of about 1,253,621 (308)

Preserving Privacy of High-Dimensional Data by l-Diverse Constrained Slicing [PDF]

, 2022
In the modern world of digitalization, data growth, aggregation and sharing have escalated drastically. Users share huge amounts of data due to the widespread adoption of Internet-of-things (IoT) and cloud-based smart devices.
Zenab Amin, Abid Khan, Khan, A., Adeel Anjum, Awais Ahmad, Anjum, A., Ahmad, A., Gwanggil Jeon, Amin, Z., Jeon, G.   +9 more
core   +1 more source

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, Rob J. Hyndman, Kate Smith-Miles   +2 more
openaire   +2 more sources

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, Andreas Mayr, Thanh Le Van, Vladimir Chupakhin, Hugo Ceulemans, Joerg Wegner, Jose-Felipe Golib-Dzib, Nina Jeliazkova, Yves Vandriessche, Stanislav Böhm, Vojtech Cima, Jan Martinovic, Nigel Greene, Tom Vander Aa, Thomas J. Ashby, Sepp Hochreiter, Ola Engkvist, Günter Klambauer, Hongming Chen   +18 more
doaj   +1 more source

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
doaj   +1 more source

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, Valery A. Makarov, Ivan Y. Tyukin   +2 more
doaj   +1 more source

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, Bogdan Grechuk, Evgeny M. Mirkes, Sergey V. Stasenko, Ivan Y. Tyukin   +4 more
doaj   +1 more source

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
doaj   +1 more source

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
doaj   +2 more sources

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, ZHENG Longjun, HE Yunbin, LI Song   +3 more
doaj   +1 more source

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.
openaire   +4 more sources