Matrix factorization techniques in machine learning, signal processing, and statistics
Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …
compressible signals. Sparse coding represents a signal as a sparse linear combination of …
Graph regularized autoencoder and its application in unsupervised anomaly detection
Dimensionality reduction is a crucial first step for many unsupervised learning tasks
including anomaly detection and clustering. Autoencoder is a popular mechanism to …
including anomaly detection and clustering. Autoencoder is a popular mechanism to …
Decision-level machinery fault prognosis using N-BEATS-based degradation feature prediction and reconstruction
Condition monitoring signals provide sufficient information about the health of machines
and, therefore, are widely used for fault diagnosis, prognosis, and health management …
and, therefore, are widely used for fault diagnosis, prognosis, and health management …
A Bregman proximal stochastic gradient method with extrapolation for nonconvex nonsmooth problems
In this paper, we explore a specific optimization problem that involves the combination of a
differentiable nonconvex function and a nondifferentiable function. The differentiable …
differentiable nonconvex function and a nondifferentiable function. The differentiable …
Wind turbine gearbox failure detection through cumulative sum of multivariate time series data
The wind energy industry is continuously improving their operational and maintenance
practice for reducing the levelized costs of energy. Anticipating failures in wind turbines …
practice for reducing the levelized costs of energy. Anticipating failures in wind turbines …
Adaptive graph-based support vector data description for weakly-supervised anomaly detection
H Wu, YF Li - IEEE Transactions on Automation Science and …, 2023 - ieeexplore.ieee.org
We propose a novel method for weakly-supervised anomaly detection, where a limited
number of labeled normal samples and a sufficient number of unlabeled samples are …
number of labeled normal samples and a sufficient number of unlabeled samples are …
Physics-Enhanced NMF Toward Anomaly Detection in Rotating Mechanical Systems
With the advancements in sensor technology, it is now possible to measure and record a
multitude of features that reflect the health condition of complex systems. These …
multitude of features that reflect the health condition of complex systems. These …
Semi-disentangled non-negative matrix factorization for rating prediction
X Zhang, X Zhou, L Chen, Y Liu - Applied Soft Computing, 2023 - Elsevier
Rating predictions have been extensively used in evaluation websites for recommendation
tasks in recent past. Due to the single data category and the simple way of feature …
tasks in recent past. Due to the single data category and the simple way of feature …
Graph regularized deep sparse representation for unsupervised anomaly detection
Anomaly detection (AD) aims to distinguish the data points that are inconsistent with the
overall pattern of the data. Recently, unsupervised anomaly detection methods have …
overall pattern of the data. Recently, unsupervised anomaly detection methods have …
Towards Multi-view Graph Anomaly Detection with Similarity-Guided Contrastive Clustering
Anomaly detection on graphs plays an important role in many real-world applications.
Usually, these data are composed of multiple types (eg, user information and transaction …
Usually, these data are composed of multiple types (eg, user information and transaction …