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.

This paper presents a polarization-aware movable antenna (PAMA) framework that integrates polarization effects into the design and optimization of movable antennas (MAs). While MAs have proven effective at boosting wireless communication performance, existing studies primarily focus on phase variations caused by different propagation paths and leverage antenna movements to maximize channel gains. This narrow focus limits the full potential of MAs. In this work, we introduce a polarization-aware channel model rooted in electromagnetic theory, unveiling a defining advantage of MAs over other wireless technologies such as precoding: the ability to optimize polarization matching. This new understanding enables PAMA to extend the applicability of MAs beyond radio-frequency, multipath-rich scenarios to higher-frequency bands, such as mmWave, even with a single line-of-sight (LOS) path. Our findings demonstrate that incorporating polarization considerations into MAs significantly enhances efficiency, link reliability, and data throughput, paving the way for more robust and efficient future wireless networks.

This paper presents a polarization-aware movable antenna (PAMA) framework that integrates polarization effects into the design and optimization of movable antennas (MAs). While MAs have proven effective at boosting wireless communication performance, existing studies primarily focus on phase variations caused by different propagation paths and leverage antenna movements to maximize channel gains. This narrow focus limits the full potential of MAs. In this work, we introduce a polarizationaware channel model rooted in electromagnetic theory, unveiling a defining advantage of MAs over other wireless technologies such as precoding: the ability to optimize polarization matching. This new understanding enables PAMA to extend the applicability of MAs beyond radio-frequency, multipath-rich scenarios to higherfrequency bands, such as mmWave, even with a single line-of-sight (LOS) path. Our findings demonstrate that incorporating polarization considerations into MAs significantly enhances efficiency, link reliability, and data throughput, paving the way for more robust and efficient future wireless networks.

This article presents a new optical integrated sensing and communication (O-ISAC) framework tailored for cost-effective light-emitting diode (LED) for enhanced Internet of Things (IoT) applications. Unlike prior research on ISAC, which predominantly focused on radio frequency (RF) band, O-ISAC capitalizes on the inherent advantages of the optical spectrum, including the ultrawide license-free bandwidth, immunity to RF interference, and energy efficiency—attributes crucial for IoT communications. The communication and sensing in our O-ISAC system unfold in two phases: 1) directionless O-ISAC and 2) directional O-ISAC. In the first phase, distributed optical access points emit nondirectional light for communication and leverage small-aperture imaging principles for sensing. In the second phase, we put forth the concept of optical beamforming, using collimating lenses to concentrate light, resulting in substantial performance enhancements in both communication and sensing. Numerical and simulation results demonstrate the feasibility and impressive performance of O-ISAC benchmarked against optical separate communication and sensing systems.

Fuzzy C-Means algorithm (FCM) is one of the most commonly used fuzzy clustering algorithm, which uses the alternating optimization algorithm to update the membership matrix and the cluster center matrix. FCM achieves effective results in clustering tasks. However, due to many constraints, the objective function is inconvenient to optimize directly and is prone to converges to a suboptimal local minimum, which affects the clustering performance. In this paper, we propose a minimization problem equivalent to FCM. Firstly, we use the optimal solution when fixing the cluster center matrix to replace the membership matrix, transforming the original constrained optimization problem into an unconstrained optimization problem, thus reducing the number of variables. We then use gradient descent instead of alternating optimization to solve the model, so we call this model UC-FCM. Extensive experimental results show that UC-FCM can obtain better local minimum and achieve superior clustering performance compared to FCM under the same initialization. Moreover, UC-FCM is also competitive compared with other advanced clustering algorithms.

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.

The clustering algorithm has always been a hot spot in machine learning, which has made great progress and been widely used in different scenarios. Due to the characteristics and requirements of some application scenarios, the branch of the balanced clustering algorithm has been developed. The ideal of these algorithms is to obtain clusters containing approximately the same number of samples. However, when there are data points distributed at the boundary of different clusters, resulting in different probabilities of their belonging, hard-partitioned balanced clustering may not be able to handle these boundary data well, thus limiting their performance. Motivated by this, we propose a Fuzzy Min-Cut with Soft Balancing Effects (FCBE) method in this article. Specifically, the FCBE model utilizes fuzzy constraints to simultaneously enhance the ability of the balanced algorithm to capture boundary data members and the advantage of directly obtaining the partitioning results of graph-cut problem without postprocessing. In addition, a sparse regularization is introduced to avoid trivial solutions and maintain the separability of the relationship matrix. Furthermore, the proposed FCBE method can be viewed as a flexibly adjustable generalization pattern that not only has clear interpretability but also can become special cases with clear physical meanings under different parameter values. The feasibility of FCBE has been verified on real datasets.

Existing deep clustering methods leverage contrastive or non-contrastive learning to facilitate downstream tasks. Most contrastive-based methods typically learn representations by comparing positive pairs (two views of the same sample) against negative pairs (views of different samples). However, we spot that this hard treatment of samples ignores inter-sample relationships, leading to class collisions and degrade clustering performances. In this paper, we propose a soft neighbor supported contrastive clustering method to address this issue. Specifically, we first introduce a concept called perception radius to quantify similarity confidence between a sample and its neighbors. Based on this insight, we design a two-level soft neighbor loss that captures both local and global neighborhood relationships. Additionally, a cluster-level loss enforces compact and well-separated cluster distributions. Finally, we conduct a pseudo-label refinement strategy to mitigate false negative samples. Extensive experiments on benchmark datasets demonstrate the superiority of our method. The code is available at https://github.com/DuannYu/soft-neighbors-supported-clustering.

Fuzzy c-means based on entropy regularization (FCER) is a commonly used machine learning algorithm that uses maximum entropy as the regularization term to realize fuzzy clustering. However, this model has many constraints and is challenging to optimize directly. During the solution process, the membership matrix and cluster centers are alternately optimized, easily converging to poor local solutions, limiting the clustering performance. In this paper, we start with the optimization model and propose an unconstrained fuzzy clustering model (UFCER) equivalent to FCER, which reduces the size of optimization variables from$(n+d)\times c$to$d\times c$. More importantly, there is no need to calculate the membership matrix during the optimization process iteratively. The time complexity is only linear, and the convergence speed is fast. We conduct extensive experiments on real datasets. The comparison of objective function value and clustering performance fully demonstrates that under the same initialization, our proposed algorithm can converge to smaller local minimums and get better clustering performance.

Deep fuzzy clustering employs neural networks to discover the low-dimensional embedding space of data, providing an effective solution to the clustering problem posed by high-dimensional data. Although some algorithms have achieved good results in application, the field still faces the following problems: the lack of clustering loss function limits the development of deep clustering, and most of them use the self-training strategy-based Kullback-Leibler (KL) divergence; some algorithms directly use conventional constrained clustering objective function as the loss function in deep models, and update network parameters alternately, the optimization process is cumbersome. Focusing on the issues mentioned above, this article first proposed an unconstrained fuzzy$c$-means algorithm that can be solved using gradient descent and then used it as the clustering loss function to obtain a novel deep fuzzy clustering model named unsupervised deep embedding for fuzzy clustering. The proposed model simultaneously learns the low-dimensional representation of data and performs fuzzy clustering. It updates parameters through gradient descent and backpropagation, achieving end-to-end optimization. The proposed algorithm's effectiveness and competitiveness are fully demonstrated through extensive experiments conducted on image and text datasets.

Discovering sequences with desired properties has long been an interesting intellectual pursuit. In pulse compression radar (PCR), discovering phase codes with low aperiodic autocorrelations is essential for a good estimation performance. The design of phase code, however, is mathematically non-trivial as the aperiodic autocorrelation properties of a sequence are intractable to characterize. In this paper, we put forth a genetic algorithm (GA) approach to discover new phase codes for PCR with the mismatched filter (MMF) receiver. The developed GA, dubbed GASeq, discovers better phase codes than the state of the art. At a code length of 59, the sequence discovered by GASeq achieves a signal-to-clutter ratio (SCR) of 50.84, while the best-known sequence has an SCR of 45.16. In addition, the efficiency and scalability of GASeq enable us to search phase codes with a longer code length, which thwarts existing deep learning-based approaches. At a code length of 100, the best phase code discovered by GASeq exhibit an SCR of 63.23.