Feature selection plays a vital role in recommender systems by identifying informative features for accurate prediction.While adaptive methods like AdaFS select instance-wise features based on sample variability, they often overlook globally important features and suffer from limited transferability.To address these limitations, we propose G2IFS (Global-to-Instance Feature Selection), a novel framework that integrates global distributional patterns with instance-level adaptation for more robust and generalizable feature selection.G2IFS consists of an online statistics module, a main scoring network, and a non-parametric Gaussian mixture module.The online statistics module maintains global estimates of class-wise statistics to compute Fisher scores, which guide the scoring network in learning instance-specific feature importance.The Gaussian module further mitigates co-adaptation and improves transferability.Extensive experiments across diverse recommendation models and real-world datasets show that G2IFS consistently outperforms state-of-the-art baselines in terms of accuracy, efficiency, and transferability.In-depth analysis further reveals that various global importance signals-when integrated into traditional methods like AdaFS-consistently lead to significant performance improvements, underscoring the general effectiveness of combining global and instance-level signals in recommender system feature selection.The code is available at https://github.com/youlj109/G2IFS.

Test-Time Adaptation (TTA) aims to enhance the generalization of deep learning models when faced with test data that exhibits distribution shifts from the training data. In this context, only a pre-trained model and unlabeled test data are available, making it particularly relevant for privacy-sensitive applications. In practice, we observe that feature redundancy in embeddings tends to increase as domain shifts intensify in TTA. However, existing TTA methods often overlook this redundancy, which can hinder the model's adaptability to new data. To address this issue, we introduce Feature Redundancy Elimination for Test-time Adaptation (FRET), a novel perspective for TTA. A straightforward approach (S-FRET) is to directly minimize the feature redundancy score as an optimization objective to improve adaptation. Despite its simplicity and effectiveness, S-FRET struggles with label shifts, limiting its robustness in real-world scenarios. To mitigate this limitation, we further propose Graph-based FRET (G-FRET), which integrates a Graph Convolutional Network (GCN) with contrastive learning. This design not only reduces feature redundancy but also enhances feature discriminability in both the representation and prediction layers. Extensive experiments across multiple model architectures, tasks, and datasets demonstrate the effectiveness of S-FRET and show that G-FRET achieves state-of-the-art performance. Further analysis reveals that G-FRET enables the model to extract non-redundant and highly discriminative features during inference, thereby facilitating more robust test-time adaptation.

Multiview features can be accessed from multiple feature extractors or frequency bands to describe polarimetric synthetic aperture radar (PolSAR) images. These multiview features usually own high dimensionality and contain some redundant and irrelevant information leading to the classification performance deterioration. Additionally, acquiring label information of PolSAR data is challenging. To resolve these difficulties, we put forward a multiview unsupervised feature selection method via view-specific and cross-view representation (VSCVR) to classify PolSAR images, resulting in retaining datas global and local structure and exploring higher-order relationship between different views. Specifically, we learn view-specific self-representation constrained by tensor nuclear norm with the aim of the global structure preservation and exploration of high-order consensus and complementarity between different views. We adopt tensor nuclear norm by tensor singular value decomposition (t-TNN) as a low-rank constraint for multiview self-representation tensor. In addition, we incorporate crossview common representation with manifold regularization to sustain the local structure information and ensure consistency across multiple views. To accurately describe the local structure, we construct a similarity graph for manifold regularization via the Wishart distance reflecting the statistical property of PolSAR data. Furthermore, a group sparsity constraint for view-specific projection matrices is utilized for feature selection. We propose the corresponding algorithm to handle VSCVR model and conduct related analysis. Numerical and visual results on some real PolSAR datasets certify that VSCVR can acquire a few important features and improve classification performance effectively.

In real-world applications, robotic systems collect vast amounts of new data from ever-changing environments over time. They need to continually interact and learn new knowledge from the external world to adapt to the environment. Particularly, lifelong object recognition in an online and interactive manner is a crucial and fundamental capability for robotic systems. To meet this realistic demand, in this article, we propose an online active continual learning (OACL) framework for robotic lifelong object recognition, in the scenario of both classes and domains changing with dynamic environments. First, to reduce the labeling cost as much as possible while maximizing the performance, a new online active learning (OAL) strategy is designed by taking both the uncertainty and diversity of samples into account to protect the information volume and distribution of data. In addition, to prevent catastrophic forgetting and reduce memory costs, a novel online continual learning (OCL) algorithm is proposed based on the deep feature semantic augmentation and a new loss-based deep model and replay buffer update, which can mitigate the class imbalance between the old and new classes and alleviate confusion between two similar classes. Moreover, the mistake bound of the proposed method is analyzed in theory. OACL allows robots to select the most representative new samples to query labels and continually learn new objects and new variants of previously learned objects from a nonindependent and identically distributed (i.i.d.) data stream without catastrophic forgetting. Extensive experiments conducted on real lifelong robotic vision datasets demonstrate that our algorithm, even trained with fewer labeled samples and replay exemplars, can achieve state-of-the-art performance on OCL tasks.

Synthetic Aperture Radar (SAR) systems work in the open and dynamic environment, and capture new data or new targets continually over time. It requires that SAR target recognition algorithms should have the capability to learn new targets incrementally without forgetting the previously learned targets. Besides, SAR targets of different classes usually have subtle differences which makes the recognition more challenging. In this paper, we propose a fine-grained continual learning algorithm for SAR incremental target recognition. Since data imbalance between old and new classes results in the forgetting of old classes, class-balanced loss is introduced to alleviate this phenomenon. In addition, covariance pooling network is utilized to explore the higher-order statistical information to improve the discrimination of features. Experimental results on real SAR datasets validate the effectiveness of the proposed method.

Polarimetric synthetic aperture radar (PolSAR) data are sequentially acquired and have multiple views obtained from different feature extractors or multiple frequency bands. The fast and accurate classification of PolSAR data in dynamically changing environments is a critical and challenging task. Online learning can handle this task by learning a classifier incrementally from a stream of samples. In this article, we propose an online semisupervised active learning framework for multiview PolSAR data classification, called OSAM. First, a novel online active learning strategy is designed based on the relationships among multiple views and a randomized rule, which allows to only query the labels of some informative incoming samples. Then, in order to utilize both the incoming labeled and unlabeled samples to update the classifiers, a novel online semisupervised learning model is proposed based on co-regularized multiview learning and graph regularization. In addition, the proposed method can deal with the dynamic large-scale multifeature or multifrequency PolSAR data where not only the amount of data but also the number of classes gradually increases in the learning process. Moreover, the mistake bound of the proposed method is derived rigorously. Extensive experiments are conducted on real PolSAR data to evaluate the performance of our algorithm, and the results demonstrate the effectiveness of the proposed method.

Image scene classification aiming to assign specific semantic labels for each image is vital important for the applications of remote sensing (RS) data. In real world, since the observation environment is open and dynamic, RS images are collected sequentially and the numbers of images and classes grow rapidly over time. Most existing scene classification methods are offline learning algorithms which are inefficient and unscalable for this scenario. In this paper, an incremental learning with open set recognition framework is proposed for RS image scene classification in the open and dynamic environment, called ILOSR, which can identify the unknown classes from a stream of data and learn these new classes incrementally. Specifically, a controllable convex hull-based exemplar selection strategy is designed to address the catastrophic forgetting issue in incremental learning, which can reduce training time and memory footprint effectively. In addition, a new loss function based on prototype learning and uncertainty measurement is proposed for open set recognition to enhance the inter-class discrimination and intra-class compactness of the learned deep features. Experimental results on real remote sensing datasets demonstrate that the proposed method can not only outperform the state-of-the-art approaches on offline classification, incremental learning and OSR problem separately, but also achieve better and more stable performance in the experiments for incremental learning with OSR.

Remote sensing data can be sequentially acquired from different sources or feature spaces, which are regarded as multiple views. For the classification task where the training data arrive in a sequence, online learning (OL) methods are effective by learning new knowledge from incoming samples incrementally. However, it is known that shallow OL models usually have limited performance. In this letter, an online multiview deep forest (OMDF) architecture is proposed, which consists of multiple layers and employs a cascade structure. Each layer is an ensemble of multiple random forests, which process data from different views, respectively. For each view, the outputs of one layer concatenated with the original feature are fed into the next layer. The proposed method learns a deep forest model in an online manner from a stream of multiview data. The structure of every random forest and the weights adjusting the importance among different views will be updated dynamically. Experimental results on multifeature or multifrequency PolSAR data and the fusion of PolSAR and optical data demonstrate that the proposed method can achieve higher test accuracy and significantly improve the performance, especially on small-scale training data, compared with the other methods.

Feature is a crucial element of polarimetric synthetic aperture radar (PolSAR) image classification. Multiple types of Features, such as polarimetric features (PF) generated from the PolSAR data and various polarimetric target decompositions, texture features (TF) of the Pauli color-coded PolSAR images are used as features for PolSAR image classification. The obtained PF and TF often form the high-dimensional data, which leads to high computational complexity. Moreover, some features are irrelative and do nothing to improve the classification performance. Therefore, it is fairly indispensable to select a subset of useful features for PolSAR image classification. This paper proposes a multi-view feature selection method for PolSAR image classification. Firstly, two types of features, PF and TF are generated separately. Then the optimization model is built to pursue the feature selection matrices. Specifically, in order to maintain the consistency of different types of features, we search for the common representation of multiple types of features in the optimization problem. The l2,1 norm sparsity regularization is imposed on the feature selection matrices to achieve feature selection. In addition, the manifold regularization on the common representation is utilized to preserve the structure information of the data. The effectiveness of the proposed method is evaluated on three real PolSAR data sets. Experimental results demonstrate the superiority of the proposed method.

Wishart distance of covariance matrices and Euclidean distance of polarimetric features are two important similarity measurements for polarimetric synthetic aperture radar (Pol-SAR) image classification. This paper proposes a feature extraction method by combing the two distances for Pol-SAR image classification. Firstly, two weight graphs are constructed based on the two distances to represent the local information of the data. Specifically, the neighbouring samples are sought in a local region to reduce the computation burden and utilize the spatial information. Then the dimensionality reduction model is constructed based on the two weight graphs and co-regularization. The co-regularization aims to minimize the dissimilarity of low-dimensional features corresponding to two graphs. Finally, the obtained low-dimensional features are used for PolSAR image classification. Experimental results on real PolSAR datasets demonstrate the effectiveness and superiority of the proposed method.

For polarimetric synthetic aperture radar (PolSAR) image classification, each pixel can be represented by multiple features from different perspectives, such as polarimetric feature (PF), texture feature (TF) and color feature (CF). Both multi-view canonical correlation analysis (MCCA) and multi-view spectral embedding (MSE) are two unsupervised multi-view subspace learning methods which search for different projection matrices for different features to combine multiple features in a common low-dimensional feature space. However, MCCA emphasizes the correlation of multiple features and MSE learns the complementarity of multiple features. To deeply learn the relation of multiple features, we incorporate MCCA with MSE based on the label information and a symmetric version of revised Wishart (SRW) distance for supervised PolSAR image feature extraction. Experimental results confirm that the proposed method can improve the classification performance.

Spatial information helps improve the performance of polarimetric synthetic aperture radar (PolSAR) image classification. Some existing methods have combined the spatial information and polarimetric features by the third-order tensor representation for feature extraction. They describe a pixel with the patch centered on this pixel. But they neglect the spatial heterogeneity, which may influence the classification performance. Therefore, we firstly seek k nearest samples based on the polarimetric feature similarity for each pixel to construct the second-order tensor, whose first order denotes the nearest samples and the second order denotes the polarimetric features. Moreover, k nearest samples are searched in a spatial local region rather than the full image, which can exploit the spatial information and reduce the computational burden. Then we employ tensor principal component analysis (TPCA) to extract low-dimensional features. Experimental results demonstrate that the proposed method can improve the classification performance compared with other methods.

The fast and accurate classification of polarimetric synthetic aperture radar (PolSAR) data in dynamically changing environments is an important and challenging task. In this paper, we propose an Incremental Multi-view Passive-Aggressive Active learning algorithm, named IMPAA, for PolSAR data classification. This algorithm can deal with online two-view multi-class categorization problem by exploiting the relationship between the polarimetric-color and texture feature sets of PolSAR data. In addition, the IMPAA algorithm can handle the dynamic large-scale datasets where not only the amount of data but also the number of classes gradually increases. Moreover, this algorithm only queries the class labels of some informative incoming samples to update the classifier based on the disagreement of different views' predictors and a randomized rule. Experiments on real PolSAR data demonstrate that the proposed method can use a smaller fraction of queried labels to achieve low online classification errors compared with previously known methods.

Feature extraction is a very important step for polarimetric synthetic aperture radar (PolSAR) image classification. Many dimensionality reduction (DR) methods have been employed to extract features for supervised PolSAR image classification. However, these DR-based feature extraction methods only consider each single pixel independently and thus fail to take into account the spatial relationship of the neighboring pixels, so their performance may not be satisfactory. To address this issue, we introduce a novel tensor local discriminant embedding (TLDE) method for feature extraction for supervised PolSAR image classification. The proposed method combines the spatial and polarimetric information of each pixel by characterizing the pixel with the patch centered at this pixel. Then each pixel is represented as a third-order tensor, of which the first two modes indicate the spatial information of the patch (i.e. the row and the column of the patch) and the third mode denotes the polarimetric information of the patch. Based on the label information of samples and the redundance of the spatial and polarimetric information, a supervised tensor-based dimensionality reduction technique, called TLDE, is introduced to find three projections which project each pixel, that is, the third-order tensor into the low-dimensional feature. Finally, classification is completed based on the extracted features using the nearest neighbor (NN) classifier and the support vector machine (SVM) classifier. The proposed method is evaluated on two real PolSAR data sets and the simulated PolSAR data sets with various number of looks. The experimental results demonstrate that the proposed method not only improves the classification accuracy greatly, but also alleviates the influence of speckle noise on classification.

Reducing samples through convex hull vertices selection (CHVS) within each class is an important and effective method for online classification problems, since the classifier can be trained rapidly with the selected samples. However, the process of CHVS is NP-hard. In this paper, we propose a fast algorithm to select the convex hull vertices, based on the convex hull decomposition and the property of projection. In the proposed algorithm, the quadratic minimization problem of computing the distance between a point and a convex hull is converted into a linear equation problem with a low computational complexity. When the data dimension is high, an approximate, instead of exact, convex hull is allowed to be selected by setting an appropriate termination condition in order to delete more nonimportant samples. In addition, the impact of outliers is also considered, and the proposed algorithm is improved by deleting the outliers in the initial procedure. Furthermore, a dimension convention technique via the kernel trick is used to deal with nonlinearly separable problems. An upper bound is theoretically proved for the difference between the support vector machines based on the approximate convex hull vertices selected and all the training samples. Experimental results on both synthetic and real data sets show the effectiveness and validity of the proposed algorithm.

Polarimetric synthetic aperture radar (PolSAR) plays an indispensable part in remote sensing. With its development and application, rapid and accurate online classification for PolSAR data becomes more and more important. PolSAR data can be depicted by different features such as polarimetric, texture and color features, which can be considered as multiple views. In this paper, we propose an online multiview learning method based on the passive aggressive algorithm, named OMVPA, for PolSAR data real-time classification. The OMVPA method makes full use of the consistency and complementary properties of different views. Experimental results on real PolSAR data demonstrate that the proposed method maintain a smaller mistake rate compared with other methods.

This letter proposes a novel multiview feature extraction method for supervised polarimetric synthetic aperture radar (PolSAR) image classification. PolSAR images can be characterized by multiview feature sets, such as polarimetric features and textural features. Canonical correlation analysis (CCA) is a well-known dimensionality reduction (DR) method to extract valuable information from multiview feature sets. However, it cannot exploit the discriminative information, which influences its performance of classification. Local discriminant embedding (LDE) is a supervised DR method, which can preserve the discriminative information and the local structure of the data well. However, it is a single-view learning method, which does not consider the relation between multiple view feature sets. Therefore, we propose local discriminant CCA by incorporating the idea of LDE into CCA. Specific to PolSAR images, a symmetric version of revised Wishart distance is used to construct the between-class and within-class neighboring graphs. Then, by maximizing the correlation of neighboring samples from the same class and minimizing the correlation of neighboring samples from different classes, we find two projection matrices to achieve feature extraction. Experimental results on the real PolSAR data sets demonstrate the effectiveness of the proposed method.

The Wishart fidelity and total variation (TV) based variational model (WisTV) with the positive definite (PD) constraint has shown to be effective for the whole PolSAR covariance data speckle reduction. However, the existing algorithms for solving the WisTV model only give approximation solutions by projecting the results onto the set of PD matrices, and their parameters depend strongly on the data. The purpose of this letter is to propose a new optimization algorithm to address the issues. To keep the uniformity of the parameters for different PolSAR data, a sigmoid function-based normalization method is designed, which ensures the applicability of the WisTV model for the normalized data. By using the orthogonal decomposition of the PD variables, the WisTV model is converted into an unconstrained optimization problem which is further transformed into a multivariable problem based on the equivalent representations of the trace and logdet functions. The alternative minimization technique is then utilized to solve the final optimization problem. The subproblems for each individual variable are convex and their solutions have explicit expressions. Moreover, the computational complexity of the algorithm is discussed. Experimental results on both synthetic and real PolSAR data demonstrate the validity of the proposed algorithm.

In this paper, we propose a nonlocal total variation (NLTV)-based variational model for polarimetric synthetic aperture radar (PolSAR) data speckle reduction. This model, named WisNLTV, is obtained based on the Wishart fidelity term and the NLTV regularization defined for the complex-valued fourth-order tensor data. Since the proposed model is non-convex, an equivalent bi-convex model is obtained using the property of conjugate functions. Then, an efficient iteration algorithm is developed to solve the equivalent bi-convex model, based on the alternating minimization and the forward-backward operator splitting technique. The proposed iteration algorithm is proved to be convergent under certain conditions theoretically and numerically. Experimental results on both synthetic and real PolSAR data demonstrate that the proposed method can effectively reduce speckle noise and, meanwhile, better preserve the details and the repetitive structures such as textures and edges, and the polarimetric scattering characteristics, compared with the other methods.

In this paper, we propose a variational model for polarimetric synthetic aperture radar (PolSAR) data speckle reduction, which is based on the complex Wishart distribution of the covariance or coherency matrix and multichannel total variation (TV) regularization defined for complex-valued matrices. By assuming the TV regularization to be a prior and taking the statistical distribution of the covariance matrix in each resolution element into account, the variational model for PolSAR covariance data speckle suppression, named WisTV-C, is derived from the maximum a posteriori estimate. A similar variational model for PolSAR coherency data speckle reduction, named WisTV-T, is also obtained. As far as we know, this is the first variational model for the whole PolSAR covariance or coherency matrix data despeckling. Since the model is nonconvex, a convex relaxation iterative algorithm is designed to solve the variational problem, based on the variable splitting and alternating minimization techniques. Experimental results on both simulated and real PolSAR data demonstrate that the proposed approach notably removes speckles in the extended uniform areas and, meanwhile, better preserves the spatial resolution, the details such as edges and point scatterers, and the polarimetric scattering characteristics, compared with other methods.