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Semi-supervised multi-sensor information fusion tailored graph embedded low-rank tensor learning machine under extremely low labeled rate
This paper investigates a demanding and meaningful task of intelligent fault diagnosis, in
which multi-sensors signals are fused for semi-supervised analysis with few labeled fault …
which multi-sensors signals are fused for semi-supervised analysis with few labeled fault …
Tensor methods in computer vision and deep learning
Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …
Tensors for data mining and data fusion: Models, applications, and scalable algorithms
Tensors and tensor decompositions are very powerful and versatile tools that can model a
wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which …
wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which …
The neuro bureau ADHD-200 preprocessed repository
Abstract In 2011, the “ADHD-200 Global Competition” was held with the aim of identifying
biomarkers of attention-deficit/hyperactivity disorder from resting-state functional magnetic …
biomarkers of attention-deficit/hyperactivity disorder from resting-state functional magnetic …
An intelligent outlier detection method with one class support tucker machine and genetic algorithm toward big sensor data in internet of things
X Deng, P Jiang, X Peng, C Mi - IEEE Transactions on Industrial …, 2018 - ieeexplore.ieee.org
Various types of sensor data can be collected by the Internet of Things (IoT). Each sensor
node has spatial attributes and may also be associated with a large number of measurement …
node has spatial attributes and may also be associated with a large number of measurement …
Support tensor machine with dynamic penalty factors and its application to the fault diagnosis of rotating machinery with unbalanced data
The fault diagnosis methods of rotating machinery based on machine learning have been
developed in the past years, such as support vector machine (SVM) and convolutional …
developed in the past years, such as support vector machine (SVM) and convolutional …
A literature survey of matrix methods for data science
M Stoll - GAMM‐Mitteilungen, 2020 - Wiley Online Library
Efficient numerical linear algebra is a core ingredient in many applications across almost all
scientific and industrial disciplines. With this survey we want to illustrate that numerical linear …
scientific and industrial disciplines. With this survey we want to illustrate that numerical linear …
A computationally efficient tensor regression network-based modeling attack on XOR arbiter PUF and its variants
XOR arbiter PUF (XOR APUF), where the outputs of multiple arbiter PUF (APUFs) are XOR-
ed, has proven to be more robust to machine learning-based modeling attacks. The reported …
ed, has proven to be more robust to machine learning-based modeling attacks. The reported …
Motor imagery EEG spectral-spatial feature optimization using dual-tree complex wavelet and neighbourhood component analysis
Background Frequency band optimization improves the performance of common spatial
pattern (CSP) in motor imagery (MI) tasks classification because MI-related …
pattern (CSP) in motor imagery (MI) tasks classification because MI-related …
Kernelized support tensor machines
In the context of supervised tensor learning, preserving the structural information and
exploiting the discriminative nonlinear relationships of tensor data are crucial for improving …
exploiting the discriminative nonlinear relationships of tensor data are crucial for improving …