Tensor networks for dimensionality reduction and large-scale optimization: Part 2 applications and future perspectives
Part 2 of this monograph builds on the introduction to tensor networks and their operations
presented in Part 1. It focuses on tensor network models for super-compressed higher-order …
presented in Part 1. It focuses on tensor network models for super-compressed higher-order …
A survey on deep learning-driven remote sensing image scene understanding: Scene classification, scene retrieval and scene-guided object detection
As a fundamental and important task in remote sensing, remote sensing image scene
understanding (RSISU) has attracted tremendous research interest in recent years. RSISU …
understanding (RSISU) has attracted tremendous research interest in recent years. RSISU …
A survey on tensor techniques and applications in machine learning
This survey gives a comprehensive overview of tensor techniques and applications in
machine learning. Tensor represents higher order statistics. Nowadays, many applications …
machine learning. Tensor represents higher order statistics. Nowadays, many applications …
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 machines for classification of hyperspectral remote sensing imagery
In recent years, the support vector machines (SVMs) have been very successful in remote
sensing image classification, particularly when dealing with high-dimensional data and …
sensing image classification, particularly when dealing with high-dimensional data and …
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 …
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 …
[책][B] Multilinear subspace learning: dimensionality reduction of multidimensional data
H Lu, KN Plataniotis, A Venetsanopoulos - 2013 - books.google.com
Due to advances in sensor, storage, and networking technologies, data is being generated
on a daily basis at an ever-increasing pace in a wide range of applications, including cloud …
on a daily basis at an ever-increasing pace in a wide range of applications, including cloud …
Linear support tensor machine with LSK channels: Pedestrian detection in thermal infrared images
Pedestrian detection in thermal infrared images poses unique challenges because of the
low resolution and noisy nature of the image. Here, we propose a mid-level attribute in the …
low resolution and noisy nature of the image. Here, we propose a mid-level attribute in the …
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 …