Results 11 to 20 of about 4,092,880 (310)
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.
europepmc +8 more sources
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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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 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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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 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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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 +9 more
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