Neuroscientific evidence reveals that human visual recognition is not an instantaneous event but a hierarchical process, where the brain constructs a holistic perception by progressively integrating simple features like edges or texture into complex scenes. Ensemble learning successfully utilizes this principle, yet existing methods typically integrate models at the decision level, neglecting the rich, complementary information within the feature space itself and thus fundamentally limiting their potential. To address this, we introduce Synergistic Semantic Boosting (S2-Boosting), a framework that employs a self-supervised hierarchical semantic learning module to decompose an image into complementary, semantically meaningful parts autonomously. These parts guide a boosting procedure where a sequence of specialized learners, each focusing on a specific semantic partition, collaboratively corrects the ensemble's errors. We further present encouraging results on real-world image datasets, highlighting the intrinsic interpretability, paving the way for more robust and transparent models.

Federated clustering addresses the critical challenge of extracting patterns from decentralized, unlabeled data. However, it is hampered by the flaw that current approaches are forced to accept a compromise between performance and privacy: transmitting embedding representations risks sensitive data leakage, while sharing only abstract cluster prototypes leads to diminished model accuracy. To resolve this dilemma, we propose Structural Privacy-Preserving Federated Graph Clustering (SPP-FGC), a novel algorithm that innovatively leverages local structural graphs as the primary medium for privacy-preserving knowledge sharing, thus moving beyond the limitations of conventional techniques. Our framework operates on a clear client-server logic; on the client-side, each participant constructs a private structural graph that captures intrinsic data relationships, which the server then securely aggregates and aligns to form a comprehensive global graph from which a unified clustering structure is derived. The framework offers two distinct modes to suit different needs. SPP-FGC is designed as an efficient one-shot method that completes its task in a single communication round, ideal for rapid analysis. For more complex, unstructured data like images, SPP-FGC+ employs an iterative process where clients and the server collaboratively refine feature representations to achieve superior downstream performance. Extensive experiments demonstrate that our framework achieves state-of-the-art performance, improving clustering accuracy by up to 10% (NMI) over federated baselines while maintaining provable privacy guarantees.

Ensemble learning methods, such as Bagging and Boosting, are well-regarded for their ability to enhance model performance by combining diverse base learners. These approaches leverage the strengths of individual models to achieve more accurate and robust predictions. However, real-world datasets often contain noise, which can significantly impair model effectiveness. This paper focuses on two prevalent and challenging types: feature noise, which can lead to fitting instability and poor generalization, and label noise, which can lead to erroneous supervision and model overfitting. Recognizing the inherent properties of ensemble learning, particularly its focus on optimizing the decision margin to improve classification accuracy, we see an opportunity to bolster ensemble model robustness. To address both feature and label noise, we propose a novel approach called Dual Geometry Margin Boosting (DGMB). This method employs two key strategies: the Decision Plane Margin (DPM), which enhances class separation, and the Hyper-Sphere Margin (HSM), which effectively filters out potentially noisy samples during the learning process. Our experiments demonstrate the impressive ability of DGMB to resist both feature and label noise. Through rigorous testing on various noise-contaminated datasets, we show that DGMB maintains strong performance and outperforms other robust Ensemble methods.

Deep models, characterized by complex structures and end-to-end optimization, proved effective in providing decision support based on real-world data. However, the lack of transparency in their decision-making process and the difficulty in interpreting the role of individual neurons limited their practical applicability in many critical and sensitive domains. Inspired by the parallels between neural networks and ensemble models, where performance was achieved through the collaboration of multiple weak learners, this article presents a novel perspective that reframes neural networks as hierarchical ensembles. We propose the hierarchical backpropagated ensemble (HBE) model, wherein each neuron functions both as a base learner and as part of an ensemble of preceding neurons. This framework applies ensemble learning techniques to neural networks, allowing each neuron to focus on specific subtasks while progressively constructing a network that meets global objectives. Experimental results on real-world data show that this hierarchical structure enhances the effectiveness of traditional ensemble models, and the ensemble-based explanations offer improved initialization and dynamically adjustable network structures, leading to more efficient training.

The representation learning of time series has a wide range of downstream tasks and applications in many practical scenarios. However, due to the complexity, spatiotemporality, and continuity of sequential stream data, compared with the representation learning of structural data such as images/videos, the time series self-supervised representation learning is even more challenging. Besides, the direct application of existing contrastive learning and masked autoencoder based approaches to time series representation learning encounters inherent theoretical limitations, such as ineffective augmentation and masking strategies. To this end, we propose a Language Pre-training guided Masking Representation Learning (LPMRL) for times series classification. Specifically, we first propose a novel language pre-training guided masking encoder for adaptively sampling semantic spatiotemporal patches via natural language descriptions and improving the discriminability of latent representations. Furthermore, we present the dual-information contrastive learning mechanism to explore both local and global information by meticulously designing high-quality hard negative samples of time series data samples. As a result, we also design various experiments, such as visualization of masking position and distribution and reconstruction error to verify the reasonability of proposed language guided masking technique. Last, we evaluate the performance of proposed representation learning via classification task conducted on 106 time series datasets, which demonstrates the effectiveness of proposed method.

Label noise in hyperspectral image classification (HIC) can severely degrade model performance by leading to incorrect predictions and overfitting, especially as erroneous labels propagate and compound throughout the training process. To address this, we propose a robust learning framework called Self-Supervised Localized Topology Consistency (SSLTC), which enforces local topology consistency to enhance model resilience against noisy labels. SSLTC captures local topology via a graph-based representation, where nodes represent samples and edges encode pairwise similarities. Predictions are propagated from topologically similar nodes to central nodes, constrained by Kullback-Leibler (KL) divergence to encourage consistent predictions and reduce sensitivity to noisy labels. Additionally, a self-supervised contrastive learning strategy is used to refine spectral-spatial representations in an unsupervised manner, further improving robustness. Extensive experiments on hyperspectral benchmark datasets with varying noise levels demonstrate the superiority of SSLTC in mitigating the adverse effects of label noise compared to state-of-the-art approaches in HIC tasks.

Ensemble clustering, which combines the information from multiple base clusterings to obtain a better partition result, has received extensive attention due to its effectiveness and robustness. Although many algorithms have been developed in recent years that have achieved impressive results in practical applications, two challenging issues in ensemble clustering remain. First, most algorithms assume that all base clusterings have the same impact on the clustering results, assigning them the same weight. This makes the clustering performance susceptible to the influence of redundant, low-quality base clusterings. Second, co-association matrix-based algorithms often rely on additional methods, such as hierarchical agglomerative clustering, to obtain the final clustering result after constructing the weighted co-association matrix. This not only complicates optimization process but also leads to the loss of some sample-similarity information during clustering. To address this problem, we propose a novel Toward Balance Adaptive Weighted Ensemble Clustering (TBAWEC) algorithm. This method transforms the ensemble clustering problem into an optimization problem, producing the final result without requiring additional clustering algorithms. Moreover, we introduce balanced technology into ensemble clustering for the first time, significantly improving the balance of clustering results. Extensive experiments on real datasets demonstrate that the proposed algorithm outperforms the most advanced ensemble and balanced clustering algorithms simultaneously.

Label noise in hyperspectral image classification (HIC) has posed a significant challenge, mainly due to the complexity of high-dimensional spectral-spatial data. Existing approaches that rely on sample predictions for verification and correction often amplify confirmation bias, leading to decision boundaries that overfit to noisy labels. To address these issues, we propose a Manifold-Aligned Consistency Contrastive Learning (MACCL) framework that establishes a mutually-guided consistency alignment mechanism between the spectral-spatial representation space and label space through manifold learning theory to combat label noise. Specifically, to mitigate the confirmation bias of noisy labels, we introduce a manifold-aligned consistency learning module. It leverages the manifold assumption in the representation space, modeling local neighborhood graphs and enforcing consistent prediction distributions via KL divergence. This aligns label-space classifications with the representation space’s local geometry, suppressing isolated noise through manifold continuity. Additionally, to combat representation degradation that causes decision boundaries to overfit noisy labels, we integrate a noise-tolerant contrastive representation learning module. By applying confidence-guided criteria, the module focuses on high-confidence sample pairs and regularizes gradients. This emphasizes clean pairs during training, boosting the model’s discriminative ability and preserving true semantic relationships. Through this mutual guidance, the contrastive learning refines the local geometric structure through discriminative representation learning, driving the representation space closer to the intrinsic data manifold, while the manifold alignment propagates geometric constraints to rectify label space corruptions. Finally, experiments on several benchmark datasets with varying levels of noise have validated the superiority of the proposed MACCL framework.

Boosting is a well-established ensemble learning approach that aims to enhance overall performance by combining multiple weak learners with a linear combination structure. It operates on the principle of using new learners to compensate for the shortcomings of previous learners and is known for its ability to reduce computational resource requirements while mitigating the risks of overfitting. However, from the perspective of convex optimization, it becomes apparent that classical boosting methods often converge to local optima rather than global optima when minimizing the target loss due to its greedy strategy. In this article, we address the issue and propose a novel optimization framework for the boosting paradigm. Our framework focuses on refining the ensemble model by further minimizing loss function through the reallocation of base learner weights, which results in a more robust and powerful learner. We have conducted experiments on various real-world and synthetic datasets, and our findings confirm that our Reweighted-Boosting model consistently outperforms its counterparts. It also exhibits an increased classification margin for the data, making it a valuable enhancement to original boosting algorithms.