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A hybrid anomaly detection method for high dimensional data [PDF]

PeerJ Computer Science, 2023
Anomaly detection of high-dimensional data is a challenge because the sparsity of the data distribution caused by high dimensionality hardly provides rich information distinguishing anomalous instances from normal instances. To address this, this article
Xin Zhang, Pingping Wei, Qingling Wang
doaj   +3 more sources

Procrustes Analysis for High-Dimensional Data. [PDF]

Psychometrika, 2022
AbstractThe 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-dimensional data.
Andreella A, Finos L.
europepmc   +8 more sources

Sparse sliced inverse regression for high dimensional data analysis [PDF]

BMC Bioinformatics, 2022
Background Dimension reduction and variable selection play a critical role in the analysis of contemporary high-dimensional data. The semi-parametric multi-index model often serves as a reasonable model for analysis of such high-dimensional data.
Haileab Hilafu, Sandra E. Safo
doaj   +2 more sources

Telescope indexing for k-nearest neighbor search algorithms over high dimensional data & large data sets [PDF]

Scientific Reports
When k-Nearest-Neighbors ( $$k$$ -NN) was conceived more than 70 years ago, computation, as we use it now, would be hardly recognizable. Since then, technology has improved by orders of magnitude, including unprecedented connectivity.
Madhavan K R, Hasan Kurban, Oguzhan M. Kulekci, Mehmet M. Dalkilic   +3 more
doaj   +2 more sources

Artificial Neural Network Model with Astrocyte-Driven Short-Term Memory

Biomimetics, 2023
In this study, we introduce an innovative hybrid artificial neural network model incorporating astrocyte-driven short-term memory. The model combines a convolutional neural network with dynamic models of short-term synaptic plasticity and astrocytic ...
Ilya A. Zimin, Victor B. Kazantsev, Sergey V. Stasenko   +2 more
doaj   +1 more source

Information Encoding in Bursting Spiking Neural Network Modulated by Astrocytes

Entropy, 2023
We investigated a mathematical model composed of a spiking neural network (SNN) interacting with astrocytes. We analysed how information content in the form of two-dimensional images can be represented by an SNN in the form of a spatiotemporal spiking ...
Sergey V. Stasenko, Victor B. Kazantsev
doaj   +1 more source

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, Chetverikov, Denis, Kato, Kengo, Koike, Yuta   +3 more
openaire   +2 more sources

Deep forecasting of translational impact in medical research

Patterns, 2022
Summary: The value of biomedical research—a $1.7 trillion annual investment—is ultimately determined by its downstream, real-world impact, whose predictability from simple citation metrics remains unquantified. Here we sought to determine the comparative
Amy P.K. Nelson, Robert J. Gray, James K. Ruffle, Henry C. Watkins, Daniel Herron, Nick Sorros, Danil Mikhailov, M. Jorge Cardoso, Sebastien Ourselin, Nick McNally, Bryan Williams, Geraint E. Rees, Parashkev Nachev   +12 more
doaj   +1 more source

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, Girard, Stéphane, Schmid, Cordelia   +2 more
openaire   +6 more sources

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, Wafae Sabbar, Abdelkrim Bekkhoucha   +2 more
doaj   +1 more source