Old film restoration remains challenging because archival footage often combines scratches, blotches, flicker, blur, and temporal artifacts, disrupting the balance among structural recovery, regional texture repair, and fine artifact suppression. We...
When the number of available training views is limited, the small quantity of images results in insufficient generation of Gaussian ellipsoids, leading to an empty Gaussian model. This constraint limits the generation of Gaussian ellipsoids within 3D...
Graph regularized non-negative matrix factorization (GNMF) is widely used in feature extraction. In the process of dimensionality reduction, GNMF can retain the internal manifold structure of data by adding a regularizer to non-negative matrix factor...
Due to the great benefit of rich spectral information, hyperspectral images (HSIs) have been successfully applied in many fields. However, some problems of concern also limit their further applications, such as high dimension and expensive labeling....
Unsupervised medical image translation remains challenging because model development often relies on unpaired training, whereas reliable evaluation requires well-matched reference images. PD-weighted and T2-weighted brain MR images provide a useful t...
The application of machine learning (ML) to medical datasets offers significant potential for improving disease prediction and patient outcomes. However, challenges such as feature redundancy, overfitting, and suboptimal model performance limit the p...
Surface electromyography (sEMG)-based gesture recognition has attracted considerable attention in intelligent prosthesis control, human–computer interaction, and rehabilitation assistance. However, practical deployment remains challenging becau...
One of the main principles of Deep Convolutional Neural Networks (CNNs) is the extraction of useful features through a hierarchy of kernels operations. The kernels are not explicitly tailored to address specific target classes but are rather optimize...
Regularization has become an important method in adversarial defense. However, the existing regularization-based defense methods do not discuss which features in convolutional neural networks (CNN) are more suitable for regularization. Thus, in this...
Dimensionality reduction (DR) methods based on graph embedding are widely used for feature extraction. For these methods, the weighted graph plays a vital role in the process of DR because it can characterize the data’s structure information. M...
CycleGAN domain transfer architectures use cycle consistency loss mechanisms to enforce the bijectivity of highly underconstrained domain transfer mapping. In this paper, in order to further constrain the mapping problem and reinforce the cycle consi...
This paper introduces logistic regression with sparse and smooth regularizations (LR-SS), a novel framework that simultaneously enhances both classification and feature extraction capabilities of standard logistic regression. By incorporating a famil...
In the field of remote sensing technology, the semantic segmentation of remote sensing images carries substantial importance. The creation of high-quality models for this task calls for an extensive collection of image data. However, the manual annot...
Missing labels are common in multi-label learning and can bias both label-correlation estimation and classifier induction. Existing missing-label methods often recover incomplete supervision mainly through global label correlations. However, correlat...
Multi-source heterogeneous data has been widely adopted in developing artificial intelligence systems in recent years. In real-world scenarios, raw multi-source data are generally unlabeled and inherently contain multi-view noise and feature redundan...
With the growing penetration of wind power into electric grids, improving wind speed prediction accuracy has become particularly valuable for the exploitation of wind power. In this paper, a novel hybrid strategy based on a three-phase signal decompo...
This paper introduces a novel semi-supervised tri-training classification algorithm based on regularized local discriminant embedding (RLDE) for hyperspectral imagery. In this algorithm, the RLDE method is used for optimal feature information extract...
Deep learning models for brain tumor classification from magnetic resonance imaging (MRI) often achieve high in-dataset accuracy but exhibit substantial performance degradation when evaluated on unseen clinical data due to domain shift arising from v...
In recent years, the growing availability of large-scale data across a wide range of disciplines has created new opportunities for developing models that improve the predictive accuracy of statistical models. Although techniques such as regularizatio...
Strawberry harvesting represents a labor-intensive agricultural operation where existing end-effector pose control algorithms frequently exhibit insufficient precision in fruit grasping, often resulting in unintended damage to target fruits. Concurre...
In the rapidly evolving field of multi-omics data analysis, understanding the stability of feature selection is critical for reliable biomarker discovery and clinical applications. This study investigates the stability of feature-selection methods ac...
Multi-label text feature selection aims to identify compact and discriminative feature subsets for documents associated with multiple correlated labels. In federated environments, this task is further complicated by high-dimensional search spaces, no...
Background/Objectives: Convolutional Neural Networks (CNNs), while effective in tasks such as image classification and language processing, often experience overfitting and inefficient training due to static, structure-agnostic regularization techniq...
Regular geometric targets under microscopic scenes, such as microspheres, micropores, and microtubes, are characterized by small scales, low contrast, and degraded boundaries. Masks generated by general segmentation methods often fail to directly sup...
Classification of Hyperspectral Images (HSIs) has gained attention for the past few decades. In remote sensing image classification, the labeled samples are insufficient or hard to obtain; however, the unlabeled ones are frequently rich and of a vast...
In this paper, we present a novel unsupervised feature selection method termed robust matrix factorization with robust adaptive structure learning (RMFRASL), which can select discriminative features from a large amount of multimedia data to improve t...
Effective credit risk assessment of heavy-polluting enterprises can achieve a balance between environmental and economic benefits. It requires the consideration of risk indicators for both the carbon information dimension and the compliance dimension...
Identifying the small group of customers who hold a given policy in severely imbalanced tabular data is a recurring screening problem in insurance analytics. This study considers binary caravan-insurance policy-ownership classification on the COIL 20...
In this paper, a non-modal parametric method to identify structural damage using a regularized autoregressive moving average time series model under environmental excitation is proposed in combination with the virtual impulse response function. This...
Small target detection is a critical step in remotely infrared searching and guiding applications. However, previously proposed algorithms would exhibit performance deterioration in the presence of complex background. It is attributed to two main rea...
Due to their high efficiency and real-time performance, discriminant correlation filtering (DCF) trackers have been widely applied in unmanned aerial vehicle (UAV) tracking. However, the robustness of existing trackers is still poor when facing compl...
The anisotropic characteristic reflects discriminating features of the geometry of a scattering center. In this study, we propose a novel method based on the iterative re-weighted Tikhonov regularization (IRWTR) to achieve the extraction of anisotrop...
Feature selection is essential in machine learning and data science. Recently, there has been a growing effort to apply various mathematical methods to construct novel feature selection algorithms. In this study, we present a comprehensive state-of-t...
GPS spoofing attacks are a severe threat to unmanned aerial vehicles. These attacks manipulate the true state of the unmanned aerial vehicles, potentially misleading the system without raising alarms. Several techniques, including machine learning, h...
In the era of big data, high-dimensional datasets have become increasingly common in fields such as biometrics, computer vision, and medical imaging. While such data contain abundant information, they are often accompanied by substantial noise, high...
The timely updating of the spatial distribution of buildings is essential to understanding a city’s development. Deep learning methods have remarkable benefits in quickly and accurately recognizing these changes. Current semi-supervised change...
Image dehazing is a traditional task, yet it still presents arduous problems, especially in the removal of haze from the texture and edge information of an image. The state-of-the-art dehazing methods may result in the loss of some visual informative...
Spatial texture features have been demonstrated to be very useful for the recently-proposed representation-based classifiers, such as the sparse representation-based classifier (SRC) and nearest regularized subspace (NRS). In this work, a weighted...
Understanding feature associations is vital for selecting the most informative features. Existing methods primarily focus on global feature associations, which capture overall relationships across all samples. However, they often overlook class-speci...
Accurate feature selection is critical for machine learning in medical diagnosis, yet conventional methods often fail to capture complex non-linear relationships in biomedical data. This study introduces an advanced feature selection approach that in...
Autofocus is an essential technique for airborne synthetic aperture radar (SAR) imaging to correct phase errors mainly due to unexpected motion error. There are several well-known conventional autofocus methods such as phase gradient autofocus (PGA)...
Visual tracking has become widespread in swarm robots for intelligent video surveillance, navigation, and autonomous vehicles due to the development of machine learning algorithms. Discriminative correlation filter (DCF)-based trackers have gained in...
The classification of wireless communication channel scenarios is vital for modern wireless technologies. Efficient data preprocessing for identification, especially starting from 5G and beyond, where multiple scenario transitions occur, is crucial....
Human lying is influenced by cognitive neural mechanisms in the brain, and conducting research on lie detection in speech can help to reveal the cognitive mechanisms of the human brain. Inappropriate deception detection features can easily lead to di...
Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at dif...
Intrusion Detection Systems (IDSs) remain essential for securing modern network infrastructures, where traffic data are often high-dimensional and contain redundant or weakly informative attributes. This study proposes a hybrid feature selection appr...
Missing modalities remain a major obstacle to the real-world deployment of multimodal learning systems, as incomplete inputs can substantially degrade model performance. Existing methods often suffer from biased imputation under high missing rates an...
Large-class few-shot learning has a wide range of applications in many fields, such as the medical, power, security, and remote sensing fields. At present, many few-shot learning methods for fewer-class scenarios have been proposed, but little resear...
A considerable number of approaches based on consistency regularization and pseudo-labeling have been proposed in Semi-supervised Learning (SSL) so far. These approaches significantly enhance the SSL methods by effectively utilizing large amount of u...
Contact-free concealed object detection using passive millimeter-wave imaging (PMMWI) sensors is a challenging task due to a low signal-to-noise ratio (SNR) and nonuniform illumination affecting the captured image’s quality. The nonuniform illu...
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