Artifacts in Simultaneous hdEEG/fMRI Imaging: A Nonlinear Dimensionality Reduction Approach [PDF]
Sensors, 2019 Simultaneous recordings of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) are at the forefront of technologies of interest to physicians and scientists because they combine the benefits of both modalities—better time ...
Marek Piorecky +4 more
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Efficient and reliable spike sorting from neural recordings with UMAP-based unsupervised nonlinear dimensionality reduction. [PDF]
PLoS BiologySpike sorting is one of the cornerstones of extracellular electrophysiology. By leveraging advanced signal processing and data analysis techniques, spike sorting makes it possible to detect, isolate, and map single neuron spiking activity from both in ...
Daniel Suárez-Barrera +11 more
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Remote Sensing, 2020 Accurate and timely information on the spatial distribution of crops is of great significance to precision agriculture and food security. Many cropland mapping methods using satellite image time series are based on expert knowledge to extract ...
Yongguang Zhai +4 more
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Distortion-Free Nonlinear Dimensionality Reduction [PDF]
, 2008 Nonlinear Dimensionality Reduction is an important issue in many machine learning areas where essentially low-dimensional data is nonlinearly embedded in some high-dimensional space. In this paper, we show that the existing Laplacian Eigenmaps method suffers from the distortion problem, and propose a new distortion-free dimensionality reduction method ...
Yangqing Jia +2 more
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Detecting Adversarial Examples through Nonlinear Dimensionality Reduction [PDF]
, 2019 Deep neural networks are vulnerable to adversarial examples, i.e., carefully-perturbed inputs aimed to mislead classification. This work proposes a detection method based on combining non-linear dimensionality reduction and density estimation techniques.
Crecchi F., Bacciu D., Biggio B.
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Quantum locally linear embedding for nonlinear dimensionality reduction [PDF]
Quantum Information Processing, 2019 Reducing the dimension of nonlinear data is crucial in data processing and visualization. The locally linear embedding algorithm (LLE) is specifically a representative nonlinear dimensionality reduction method with maintaining well the original manifold ...
Xi He, Li Sun, Chufan Lyu, Xiaoting Wang
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Dimensionality Reduction Nonlinear Partial Least Squares Method for Quality-Oriented Fault Detection [PDF]
MathematicsUnlike traditional fault detection methods, quality-oriented fault detection further classifies the types of faults into quality-related and non-quality-related faults.
Jie Yuan, Hao Ma, Yan Wang
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Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction [PDF]
BMC Bioinformatics, 2018 There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused ...
Kevin Faust +6 more
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Research on a multivariate measurement system for polyethylene gas pipelines utilizing a variable weight UMAP model [PDF]
Scientific ReportsIn producing gas-polyethylene pipelines, the five major quality indicators—wall thickness, inner diameter, outer diameter, concentricity, and ovality—exhibit complex interactions, making it challenging for traditional methods to comprehensively evaluate ...
Chenjia Zong +5 more
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