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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
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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
Dynamic Feature Selection for Clustering High Dimensional Data Streams [PDF]
IEEE Access, 2019 Change in a data stream can occur at the concept level and at the feature level. Change at the feature level can occur if new, additional features appear in the stream or if the importance and relevance of a feature changes as the stream progresses. This
Conor Fahy, Shengxiang Yang
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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
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Telescope indexing for k-nearest neighbor search algorithms over high dimensional data & large data sets [PDF]
Scientific ReportsWhen 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 +3 more
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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 +2 more
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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
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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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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 +12 more
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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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