A comprehensive survey on regularization strategies in machine learning
Y Tian, Y Zhang - Information Fusion, 2022 - Elsevier
In machine learning, the model is not as complicated as possible. Good generalization
ability means that the model not only performs well on the training data set, but also can …
ability means that the model not only performs well on the training data set, but also can …
Background subtraction in real applications: Challenges, current models and future directions
Computer vision applications based on videos often require the detection of moving objects
in their first step. Background subtraction is then applied in order to separate the background …
in their first step. Background subtraction is then applied in order to separate the background …
Infrared dim and small target detection via multiple subspace learning and spatial-temporal patch-tensor model
Robust detection of infrared small and dim targets with highly heterogeneous backgrounds
plays an indispensable role in infrared search and tracking (IRST) system, which is still a …
plays an indispensable role in infrared search and tracking (IRST) system, which is still a …
Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data
Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …
Enhanced tensor RPCA and its application
Despite the promising results, tensor robust principal component analysis (TRPCA), which
aims to recover underlying low-rank structure of clean tensor data corrupted with …
aims to recover underlying low-rank structure of clean tensor data corrupted with …
Moving object detection in complex scene using spatiotemporal structured-sparse RPCA
Moving object detection is a fundamental step in various computer vision applications.
Robust principal component analysis (RPCA)-based methods have often been employed for …
Robust principal component analysis (RPCA)-based methods have often been employed for …
[HTML][HTML] Review of dimension reduction methods
Purpose: This study sought to review the characteristics, strengths, weaknesses variants,
applications areas and data types applied on the various Dimension Reduction techniques …
applications areas and data types applied on the various Dimension Reduction techniques …
Factor group-sparse regularization for efficient low-rank matrix recovery
This paper develops a new class of nonconvex regularizers for low-rank matrix recovery.
Many regularizers are motivated as convex relaxations of the\emph {matrix rank} function …
Many regularizers are motivated as convex relaxations of the\emph {matrix rank} function …
Hyperspectral anomaly detection with tensor average rank and piecewise smoothness constraints
Anomaly detection in hyperspectral images (HSIs) has attracted considerable interest in the
remote-sensing domain, which aims to identify pixels with different spectral and spatial …
remote-sensing domain, which aims to identify pixels with different spectral and spatial …
Foreground gating and background refining network for surveillance object detection
Detecting objects in surveillance videos is an important problem due to its wide applications
in traffic control and public security. Existing methods tend to face performance degradation …
in traffic control and public security. Existing methods tend to face performance degradation …