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.

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.

Ensemble clustering using co-association matrices integrates multiple base clusterings but often overlooks interactions between crucial samples and base clusterings. This neglect can introduce noise and lead to information loss and instability. To address these issues, we propose the Consensus Graph-Based Spectral Ensemble Clustering via Low-Rank Tensor Learning (SECGTL) model. SECGTL organizes base clusterings into a third-order tensor and applies the Fast Fourier Transform (FFT) to capture inter-relations in the frequency domain. By rotating the tensor and minimizing the Tensor Schatten p-norm, SECGTL extracts shared information in a low-rank space, reducing noise and enhancing the learned common graph. With Laplacian rank constraints, SECGTL directly learns a graph with c-connected components, representing the clustering structure without post-processing. Extensive experiments on real-world datasets demonstrate SECGTL’s superior performance and robustness to noise.

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.

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.

In the realm of high-dimensional data analysis, the existence of outliers presents a substantial hurdle to the efficacy of feature selection methods that rely on the assumption of Gaussian distribution. To tackle this issue, we propose an outlier-robust feature selection method, ORFS, which combines robust ℓ2,1-norm minimization with group row-sparsity induced constrains to achieve both robustness and discriminative prediction capabilities. Moreover, the group row-sparsity constraints subspace learning based on ℓ2,0-norm can directly select features without parameter tuning. Finally, we introduce an iterative optimization strategy to solve NP-hard problem, and extensive experiments demonstrate the efficacy of ORFS in effectively eliminating the impact of outliers and significantly improving classification performance.