Imbalance in multi-domain multi-modal settings remains a significant challenge in real-world applications, such as fake news detection. Although existing methods have enhanced semantic representations and employed complex architectures to improve recognition performance, most of them focus on domain heterogeneity and modality sensitivity. However, these methods neglect the class imbalance, which severely impacts model robustness and generalization in open-world scenarios. Learning for such imbalanced multi-domain multi-modal data is crucial but rarely studied, particularly when the domain and modal of samples are imbalanced. To address these challenges, we propose a novel Imbalanced multi-domain multi-modal learning method with Expert Collaboration and Dynamic Fusion, named IECDF. Specifically, to alleviate inter-domain imbalance, we design an expert collaboration module with a domain-shared specific embedding structure and an improved gating strategy to enhance the discriminative power of the cross-domain features. Besides, a dynamic fusion mechanism based on Mamba-Former is designed to learn adaptive weights for each modality. Moreover, to tackle the intra-class imbalance problem, we adopt asymmetric re-weighted loss—DLINEX—to increase the contributions of minority class samples and learn the unbiased decision boundary. Extensive experimental results on various datasets validate the superiority of our proposed IECDF compared to state-of-the-art methods. Our code will be available at https://github.com/Yuchen-zh/IECDF .

Label distribution learning (LDL) has large practical application potentials due to its superiority in dealing with ambiguous label information. Most existing LDL methods are designed in a closed environment, wherein all the elements, e.g., feature and label space, are fixed. Nevertheless, in reality, data are dynamically acquired in the open environment, wherein the feature space can accumulate over time and the label space can be further enriched and refined accordingly with the accumulated feature space. Conducting LDL for such simultaneous augmentation of feature and label is crucial but rarely studied, particularly when the labeled samples with full observations are limited. In this paper, we propose a novel Incremental Label Distribution Learning (ILDL) method to tackle this brand new LDL problem by continuously transiting discriminative information from the previous model to the current one. Concretely, a prior compensation regularization is designed for such discriminative information transitivity. In this manner, the current model has the capacity to reuse the previous model to guide its own training. Furthermore, we present the theoretical analyses about the generalization bound, which provides guarantees for model inheritance. Comprehensive experimental studies validate the effectiveness of our proposal.

Learning from data streams collected sequentially over time are widely spread in real-world applications. Previous methods typically assume that the data stream has a feature space with a fixed or clearly defined evolution pattern, as well as a balanced class distribution. However, in many practical scenarios, such as environmental monitoring systems, the frequency of anomalous events is significantly imbalanced compared to normal ones and the feature space dynamically changes due to ecological evolution and sensor lifespan. To alleviate this important but rarely studied problem, we propose the Adaptive Learning in Imbalace data streams with Unpredictable feature evolution (ALIU) algorithm. As data streams with imbalanced class distribution arrive, ALIU first mitigates the model's bias for the majority class by reweighting the adaptive gradient descent magnitudes between different classes. Then, a new loss function is proposed that simultaneously focuses on misclassifications and maintains model robustness. Further, when imbalanced data streams arrive with feature evolutions, we reuse the previously learned model and update the incomplete and augmented features by adopting the adaptive gradient strategy and ensemble method, respectively. Finally, we utilize the projected technique to build a sparse yet efficient model. Based on a few common and mild assumptions, we theoretically analyze that the ALIU satisfies a sub-linear regret bound under both convex and strong convex loss functions and the performance of model can be improved with the assistance of old features. Besides, extensive experimental results further demonstrate the effectiveness of our proposed algorithm.

Pairwise constrained clustering, which employs the pairwise constraints to boost clustering performance, has been widely used in many applications such as face clustering and image retrieval. Due to the prevalence of multi-view data, pairwise constrained multi-view clustering has attracted increasing attention. Nevertheless, existing methods suffer from at least one of the three issues, i.e., expensive time consumption, two-stage clustering and inadequate use of pairwise constraints. To address the above issues, this paper proposes a Pairwise Constrained Bipartite Graph (PCBG) learning method for efficient one-step pairwise constrained multi-view clustering. Concretely, to encode must-link constraints, a novel comprehensive bipartite graph is elegantly designed. Meanwhile, a cannot-link regularization is derived and imposed on the comprehensive bipartite graph, which enforces cannot-link constraints to be realized with theoretically provable guarantees. Moreover, the comprehensive bipartite graph is constrained to exhibit explicit clustering partition by its connected components. Then, an efficient and convergent algorithm with theoretically proved accelerating techniques is derived for optimization, which has linear time complexity to the sample size. Extensive experimental results demonstrate the advantages of PCBG in both clustering performance and time complexity compared with state-of-the-art baselines.

Due to the timeliness and uncertainty of data acquisition, label shift, which assumes that the source (training) and target (test) label distributions differ, occurs with the changing environment and reduces the generalization ability of traditional models. To correct the label shift, existing methods estimate the true label distribution by prediction of target data from a source classifier, which results in high variance, especially with large label shift. In this paper, we tackle this problem by proposing a novel approach termed as Label Shift Correction via Bidirectional Marginal Distribution Matching (BMDM). Our approach matchs the label and feature marginal distributions simultaneously to ensure the stability of estimated class proportions. We prove theoretically that there is a unique optimal solution, i.e., true target label distribution, for our approach under mild conditions, and an efficient optimization strategy is also proposed. On this basis, in multi-shot scenario where label distribution changes continuously, we extend BMDM by designing a new distribution matching mechanism and constructing a regularization term that constrains the direction of label distribution change. Extensive experimental results validate the effectiveness of our approach over existing state-of-the-arts methods.

Multi-instance Learning (MIL) is a popular learning paradigm arising from many real applications. It assigns a label to a set of instances, which is called a bag, and the bag’s label is determined by the instances within it. A bag is positive if and only if it has at least one positive instance. Since labeling bags is more complicated than labeling each instance, we will often face the mislabeling problem in MIL. Furthermore, it is more common that a negative bag has been mislabeled to a positive one, since one mislabeled instance will lead to the change of the whole bag label. This is an important problem that originated from real applications, e.g., web mining and image classification, but little research has concentrated on it as far as we know. In this article, we focus on this MIL problem with one side label noise that the negative bags are mislabeled as positive ones. To address this challenging problem, we propose, to the best our our knowledge, a novel multi-instance learning method with one side label noise. We design a new double weighting approach under traditional framework to characterize the “faithfulness” of each instance and each bag in learning the classifier. Briefly, on the instance level, we employ a sparse weighting method to select the key instances, and the MIL problem with one size label noise is converted to a mislabeled supervised learning scenario. On the bag level, the weights of bags, together with the selected key instances, will be utilized to identify the real positive bags. In addition, we have solved our proposed model by an alternative iteration method with proved convergence behavior. Empirical studies on various datasets have validated the effectiveness of our method.

Online learning is a fundamental paradigm for learning from continuous data stream. Tradition online learning approaches usually assume that the feature space of data stream is fixed and the incoming instance can always get the true label after making its prediction. However, in many real-world applications, such as the personalized recommender systems, the feature space may keep expanding due to the accumulation of user behaviors. Besides, we may only get bandit feedback, i.e., we only know whether the prediction is correct or not. To solve this important but rarely studied problem, we propose a novel algorithm LIFBF, together with its two variants LIFBF-I and LIFBF-II, to learn from data stream with incremental feature space and bandit feedback. Specifically, when an instance arrives with augmented features, we first utilize the exploration-exploitation strategy to guess its best label, then, a new loss function considering both bandit feedback and guessed label is proposed. Finally, we design a highly dynamic multi-class classifier, which updates the shared and augmented features by adopting the passive-aggressive rule and structural risk minimization principle, respectively. We theoretically analyze the cumulative loss bound of LIFBF. Besides, empirical studies on various datasets further validate the effectiveness of our proposed algorithms.

The scarcity of labels is common and great challenge in traditional supervised learning. Semi-supervised learning (SSL) leverages unlabeled samples to alleviate the absence of label information. Similar with annotation, label proportion is another type of prior information and plays a significant role in classification tasks. Compared with the acquisition of labels, label proportion can be obtained more easily. For example, only a small number of patients have been diagnosed with or not with cancers in hospital database, while the proportion with cancer can be generally estimated by historical records. How to incorporate such prior information of label proportion is crucial but rarely studied in literature. Traditional SSL methods often ignore this prior information and will lead to performance degradation inevitably. To solve this problem, we propose a novel SSL with Label Proportion (SSLLP). Our approach encourages to preserve label consistency and label proportion by imposing the cardinality bound constraints. Our formulated problem equals to a mixed-integer constrained submodular minimization and it is difficult to be solved directly. Therefore, we transformed the original problem into a convex one by Lov$\acute{\text{a}}$sz extension and designed an efficient solving algorithm. Extensive experimental results present the improved performance of our method over several state-of-the-art methods.

Imbalanced clustering, where the number of samples varies in different clusters, has arisen from many real data mining applications. It has gained increasing attention. Nevertheless, due to its unsupervised nature, imbalanced clustering is more challenging than its supervised counterpart, i.e., imbalanced classification. Furthermore, existing imbalanced clustering methods are empirically designed and they often lack solid theoretical guarantees, e.g., the excess risk estimation. To solve these important but rarely studied problems, we first propose a novel$k$-Means algorithm for imbalanced clustering problem with Adaptive Cluster Weight (MACW), together with its excess clustering risk bound analysis. Inspired by this theoretical result, we further propose an improved algorithm called Imbalanced Clustering with Theoretical Learning Bounds (ICTLB). It refines the weights and encourages the optimal trade-off among per-cluster weights by optimizing the excess clustering risk bound. A theoretically-principled justification of ICTLB is provided for verification. Comprehensive experiments on many imbalanced datasets verify the effectiveness of ICTLB in solving cluster imbalanced problems.

Recently, learning and mining from data streams with incremental feature spaces have attracted extensive attention, where data may dynamically expand over time in both volume and feature dimensions. Existing approaches usually assume that the incoming instances can always receive true labels. However, in many real-world applications, e.g., environment monitoring, acquiring the true labels is costly due to the need of human effort in annotating the data. To tackle this problem, we propose a novel incremental Feature spaces Learning with Label Scarcity (FLLS) algorithm, together with its two variants. When data streams arrive with augmented features, we first leverage the margin-based online active learning to select valuable instances to be labeled and thus build superior predictive models with minimal supervision. After receiving the labels, we combine the online passive-aggressive update rule and margin-maximum principle to jointly update the dynamic classifier in the shared and augmented feature space. Finally, we use the projected truncation technique to build a sparse but efficient model. We theoretically analyze the error bounds of FLLS and its two variants. Also, we conduct experiments on synthetic data and real-world applications to further validate the effectiveness of our proposed algorithms.

Partial multi-view clustering has attracted various attentions from diverse fields. Most existing methods adopt separate steps to obtain unified representations and extract clustering indicators. This separate manner prevents two learning processes to negotiate to achieve optimal performance. In this paper, we propose the Joint Representation Learning and Clustering (JRLC) framework to address this issue. The JRLC framework employs representation matrices to extract view-specific clustering information directly from the presence of partial similarity matrices, and rotates them to learn a common probability label matrix simultaneously, which connects representation learning and clustering seamlessly to achieve better clustering performance. Under the guidance of JRLC framework, several new incomplete multi-view clustering methods can be developed by extending existing single-view graph-based representation learning methods. For illustration, within the framework, we propose two specific methods, JRLC with spectral embedding (JRLC-SE) and JRLC via integrating nonnegative embedding and spectral embedding (JRLC-NS). Two iterative algorithms with guaranteed convergence are designed to solve the resultant optimization problems of JRLC-SE and JRLC-NS. Experimental results on various datasets and news topic clustering application demonstrate the effectiveness of the proposed algorithms.

Feature selection plays a significant role in dealing with high-dimensional data to avoid the curse of dimensionality. In many real applications, like video semantic recognition, handling few labeled and large unlabeled data samples from the same population is a recently addressed challenge in feature selection. To solve this problem, we propose a novel semi-supervised feature selection method via insensitive sparse regression (ISR). Specifically, we compute the soft label matrix by the special label propagation, which can predict the labels of the unlabeled data. To guarantee the robustness of ISR to the false labeled instances or outliers, we propose Insensitive Regression Model (IRM) by capped$l_2$-$l_p$-norm loss. The soft label is imposed as the weights of IRM to fully utilize the label information. Meanwhile, to perform feature selection, we incorporate$l_{2,q}$-norm regularizer with IRM as the structural sparsity constraint when$0 < q\leq 1$. Moreover, we put forward an effective approach for solving the formulated non-convex optimization problem. We analyze the performance of convergence rigorously and discuss the parameter determination problem. Extensive experimental results on several public data sets verify the effectiveness of our proposed algorithm in comparison with the state-of-art feature selection methods. Finally, we apply our method to video semantic recognition successfully.

With the advent of multi-view data, multi-view learning has become an important research direction in both machine learning and data mining. Considering the difficulty of obtaining labeled data in many real applications, we focus on the multi-view unsupervised feature selection problem. Traditional approaches all characterize the similarity by fixed and pre-defined graph Laplacian in each view separately and ignore the underlying common structures across different views. In this paper, we propose an algorithm named Multi-view Unsupervised Feature Selection with Adaptive Similarity and View Weight (ASVW) to overcome the above mentioned problems. Specifically, by leveraging the learning mechanism to characterize the common structures adaptively, we formulate the objective function by a common graph Laplacian across different views, together with the sparse ℓ2,p-norm constraint designed for feature selection. We develop an efficient algorithm to address the non-smooth minimization problem and prove that the algorithm will converge. To validate the effectiveness of ASVW, comparisons are made with some benchmark methods on real-world datasets. We also evaluate our method in the real sports action recognition task. The experimental results demonstrate the effectiveness of our proposed algorithm.

Many feature extraction methods reduce the dimensionality of data based on the input graph matrix. The graph construction which reflects relationships among raw data points is crucial to the quality of resulting low-dimensional representations. To improve the quality of graph and make it more suitable for feature extraction tasks, we incorporate a new graph learning mechanism into feature extraction and add an interaction between the learned graph and the low-dimensional representations. Based on this learning mechanism, we propose a novel framework, termed as unsupervised single view feature extraction with structured graph (FESG), which learns both a transformation matrix and an ideal structured graph containing the clustering information. Moreover, we propose a novel way to extend FESG framework for multi-view learning tasks. The extension is named as unsupervised multiple views feature extraction with structured graph (MFESG), which learns an optimal weight for each view automatically without requiring an additional parameter. To show the effectiveness of the framework, we design two concrete formulations within FESG and MFESG, together with two efficient solving algorithms. Promising experimental results on plenty of real-world datasets have validated the effectiveness of our proposed algorithms.

Multi-label data with high dimensionality arise frequently in data mining and machine learning. It is not only time consuming but also computationally unreliable when we use high-dimensional data directly. Supervised dimensionality reduction approaches are based on the assumption that there are large amounts of labeled data. It is infeasible to label a large number of training samples in practice especially in multi-label learning. To address these challenges, we propose a novel algorithm, namely Semi-Supervised Multi-Label Dimensionality Reduction (SSMLDR), which can utilize the information from both labeled data and unlabeled data in an effective way. First, the proposed algorithm enlarges the multi-label information from the labeled data to the unlabeled data through a special designed label propagation method. It then learns a transformation matrix to perform dimensionality reduction by incorporating the enlarged multi-label information. Extensive experiments on a broad range of datasets validate the effectiveness of our approach against other well-established algorithms.