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Joint computation offloading and resource allocation for end-edge collaboration in internet of vehicles via multi-agent reinforcement learning. 通过多代理强化学习实现车联网终端协作的联合计算卸载和资源分配。
IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-11-01 Epub Date: 2024-08-08 DOI: 10.1016/j.neunet.2024.106621
Cong Wang, Yaoming Wang, Ying Yuan, Sancheng Peng, Guorui Li, Pengfei Yin

Vehicular edge computing (VEC), a promising paradigm for the development of emerging intelligent transportation systems, can provide lower service latency for vehicular applications. However, it is still a challenge to fulfill the requirements of such applications with stringent latency requirements in the VEC system with limited resources. In addition, existing methods focus on handling the offloading task in a certain time slot with statically allocated resources, but ignore the heterogeneous tasks' different resource requirements, resulting in resource wastage. To solve the real-time task offloading and heterogeneous resource allocation problem in VEC system, we propose a decentralized solution based on the attention mechanism and recurrent neural networks (RNN) with a multi-agent distributed deep deterministic policy gradient (AR-MAD4PG). First, to address the partial observability of agents, we construct a shared agent graph and propose a periodic communication mechanism that enables edge nodes to aggregate information from other edge nodes. Second, to help agents better understand the current system state, we design an RNN-based feature extraction network to capture the historical state and resource allocation information of the VEC system. Thirdly, to tackle the challenges of excessive joint observation-action space and ineffective information interference, we adopt the multi-head attention mechanism to compress the dimension of the observation-action space of agents. Finally, we build a simulation model based on the actual vehicle trajectories, and the experimental results show that our proposed method outperforms the existing approaches.

车载边缘计算(Vehicular Edge Computing,VEC)是发展新兴智能交通系统的一个前景广阔的范例,它可以为车载应用提供更低的服务延迟。然而,在资源有限的 VEC 系统中,如何满足此类应用对延迟的严格要求仍是一项挑战。此外,现有方法侧重于在某个时隙内利用静态分配的资源处理卸载任务,但忽略了异构任务对资源的不同需求,造成资源浪费。为了解决 VEC 系统中的实时任务卸载和异构资源分配问题,我们提出了一种基于注意力机制和递归神经网络(RNN)的多代理分布式深度确定性策略梯度(AR-MAD4PG)的分散式解决方案。首先,为了解决代理的部分可观测性问题,我们构建了一个共享代理图,并提出了一种定期通信机制,使边缘节点能够汇总来自其他边缘节点的信息。其次,为了帮助代理更好地了解当前系统状态,我们设计了基于 RNN 的特征提取网络,以捕捉 VEC 系统的历史状态和资源分配信息。第三,针对联合观测-行动空间过大和无效信息干扰的挑战,我们采用多头关注机制来压缩代理的观测-行动空间维度。最后,我们建立了基于实际车辆轨迹的仿真模型,实验结果表明我们提出的方法优于现有方法。
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引用次数: 0
Multi-focus image fusion with parameter adaptive dual channel dynamic threshold neural P systems. 采用参数自适应双通道动态阈值神经 P 系统的多焦点图像融合。
IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-11-01 Epub Date: 2024-08-08 DOI: 10.1016/j.neunet.2024.106603
Bo Li, Lingling Zhang, Jun Liu, Hong Peng, Qianying Wang, Jiaqi Liu

Multi-focus image fusion (MFIF) is an important technique that aims to combine the focused regions of multiple source images into a fully clear image. Decision-map methods are widely used in MFIF to maximize the preservation of information from the source images. While many decision-map methods have been proposed, they often struggle with difficulties in determining focus and non-focus boundaries, further affecting the quality of the fused images. Dynamic threshold neural P (DTNP) systems are computational models inspired by biological spiking neurons, featuring dynamic threshold and spiking mechanisms to better distinguish focused and unfocused regions for decision map generation. However, original DTNP systems require manual parameter configuration and have only one stimulus. Therefore, they are not suitable to be used directly for generating high-precision decision maps. To overcome these limitations, we propose a variant called parameter adaptive dual channel DTNP (PADCDTNP) systems. Inspired by the spiking mechanisms of PADCDTNP systems, we further develop a new MFIF method. As a new neural model, PADCDTNP systems adaptively estimate parameters according to multiple external inputs to produce decision maps with robust boundaries, resulting in high-quality fusion results. Comprehensive experiments on the Lytro/MFFW/MFI-WHU dataset show that our method achieves advanced performance and yields comparable results to the fourteen representative MFIF methods. In addition, compared to the standard DTNP systems, PADCDTNP systems improve the fusion performance and fusion efficiency on the three datasets by 5.69% and 86.03%, respectively. The codes for both the proposed method and the comparison methods are released at https://github.com/MorvanLi/MFIF-PADCDTNP.

多焦点图像融合(MFIF)是一项重要技术,旨在将多个源图像的焦点区域融合成一幅完全清晰的图像。决策图方法被广泛应用于 MFIF,以最大限度地保留源图像的信息。虽然已经提出了很多判定图方法,但它们往往难以确定焦点和非焦点的边界,从而进一步影响了融合图像的质量。动态阈值神经 P(DTNP)系统是一种受生物尖峰神经元启发的计算模型,具有动态阈值和尖峰机制,能更好地区分聚焦和非聚焦区域以生成决策图。然而,最初的 DTNP 系统需要手动配置参数,而且只有一个刺激。因此,它们不适合直接用于生成高精度的决策图。为了克服这些限制,我们提出了一种名为参数自适应双通道 DTNP(PADCDTNP)系统的变体。受 PADCDTNP 系统尖峰机制的启发,我们进一步开发了一种新的 MFIF 方法。作为一种新的神经模型,PADCDTNP 系统能根据多个外部输入自适应地估计参数,生成具有稳健边界的决策图,从而获得高质量的融合结果。在 Lytro/MFFW/MFI-WHU 数据集上进行的综合实验表明,我们的方法实现了先进的性能,其结果可与 14 种具有代表性的 MFIF 方法相媲美。此外,与标准 DTNP 系统相比,PADCDTNP 系统在三个数据集上的融合性能和融合效率分别提高了 5.69% 和 86.03%。拟议方法和比较方法的代码发布在 https://github.com/MorvanLi/MFIF-PADCDTNP 上。
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引用次数: 0
An information-theoretic perspective of physical adversarial patches. 物理对抗补丁的信息论视角。
IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-11-01 Epub Date: 2024-08-03 DOI: 10.1016/j.neunet.2024.106590
Bilel Tarchoun, Anouar Ben Khalifa, Mohamed Ali Mahjoub, Nael Abu-Ghazaleh, Ihsen Alouani

Real-world adversarial patches were shown to be successful in compromising state-of-the-art models in various computer vision applications. Most existing defenses rely on analyzing input or feature level gradients to detect the patch. However, these methods have been compromised by recent GAN-based attacks that generate naturalistic patches. In this paper, we propose a new perspective to defend against adversarial patches based on the entropy carried by the input, rather than on its saliency. We present Jedi, a new defense against adversarial patches that tackles the patch localization problem from an information theory perspective; leveraging the high entropy of adversarial patches to identify potential patch zones, and using an autoencoder to complete patch regions from high entropy kernels. Jedi achieves high-precision adversarial patch localization and removal, detecting on average 90% of adversarial patches across different benchmarks, and recovering up to 94% of successful patch attacks. Since Jedi relies on an input entropy analysis, it is model-agnostic, and can be applied to off-the-shelf models without changes to the training or inference of the models. Moreover, we propose a comprehensive qualitative analysis that investigates the cases where Jedi fails, comparatively with related methods. Interestingly, we find a significant core failure cases among the different defenses share one common property: high entropy. We think that this work offers a new perspective to understand the adversarial effect under physical-world settings. We also leverage these findings to enhance Jedi's handling of entropy outliers by introducing Adaptive Jedi, which boosts performance by up to 9% in challenging images.

在各种计算机视觉应用中,真实世界中的对抗性补丁已被证明能成功破坏最先进的模型。现有的大多数防御方法都依赖于分析输入或特征级梯度来检测补丁。然而,最近基于 GAN 的攻击破坏了这些方法,因为这种攻击会生成自然补丁。在本文中,我们提出了一个新的视角,即基于输入所携带的熵而非显著性来防御对抗性补丁。我们提出的 Jedi 是一种新的抵御对抗性补丁的方法,它从信息论的角度解决补丁定位问题;利用对抗性补丁的高熵来识别潜在的补丁区域,并使用自动编码器从高熵内核中完成补丁区域的识别。Jedi 实现了高精度的对抗性补丁定位和移除,在不同的基准测试中平均能检测到 90% 的对抗性补丁,并能恢复高达 94% 的成功补丁攻击。由于 Jedi 依靠的是输入熵分析,因此与模型无关,可以应用于现成的模型,而无需改变模型的训练或推理。此外,我们还提出了一项全面的定性分析,研究了绝地与相关方法相比失效的情况。有趣的是,我们发现不同的防御方法都有一个重要的核心失败案例,那就是高熵。我们认为,这项工作为理解物理世界环境下的对抗效应提供了一个新视角。我们还利用这些发现,通过引入自适应绝地,增强了绝地对熵异常值的处理能力,从而在具有挑战性的图像中将性能提高了 9%。
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引用次数: 0
A survey on representation learning for multi-view data 多视角数据表示学习调查。
IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-11-01 DOI: 10.1016/j.neunet.2024.106842
Yalan Qin , Xinpeng Zhang , Shui Yu , Guorui Feng
Multi-view clustering has become a rapidly growing field in machine learning and data mining areas by combining useful information from different views for last decades. Although there have been some surveys based on multi-view clustering, most of these works ignore simultaneously taking the self-supervised and non-self supervised multi-view clustering into consideration. We give a novel survey for sorting out the existing algorithms of multi-view clustering in this work, which can be classified into two different categories, i.e., non-self supervised and self-supervised multi-view clustering. We first review the representative approaches based on the non-self supervised multi-view clustering, which consist of methods based on non-representation learning and representation learning. Furthermore, the methods built on non-representation learning contain works based on matrix factorization, kernel and other non-representation learning. Methods based on representation learning consist of multi-view graph clustering, deep representation learning and multi-view subspace clustering. For the methods based on self-supervised multi-view clustering, we divide them into contrastive methods and generative methods. Overall, this survey attempts to give an insightful overview regarding the developments in the multi-view clustering field.
过去几十年来,多视图聚类通过结合来自不同视图的有用信息,已成为机器学习和数据挖掘领域中一个快速发展的领域。虽然已有一些基于多视图聚类的研究,但这些研究大多忽略了同时考虑自监督和非自监督多视图聚类。在本研究中,我们对现有的多视图聚类算法进行了新颖的梳理,并将其分为两个不同的类别,即非自我监督多视图聚类和自我监督多视图聚类。我们首先回顾了基于非自我监督多视图聚类的代表性方法,这些方法包括基于非表征学习和表征学习的方法。此外,基于非表示学习的方法还包括基于矩阵因式分解、核学习和其他非表示学习的方法。基于表征学习的方法包括多视图聚类、深度表征学习和多视图子空间聚类。对于基于自监督多视图聚类的方法,我们将其分为对比方法和生成方法。总之,本调查报告试图对多视图聚类领域的发展进行深入概述。
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引用次数: 0
Toward high-quality pseudo masks from noisy or weak annotations for robust medical image segmentation 从噪声或弱注释中提取高质量伪掩码,实现稳健的医学图像分割。
IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-11-01 DOI: 10.1016/j.neunet.2024.106850
Zihang Huang , Zhiwei Wang , Tianyu Zhao , Xiaohuan Ding , Xin Yang
Deep learning networks excel in image segmentation with abundant accurately annotated training samples. However, in medical applications, acquiring large quantities of high-quality labeled images is prohibitively expensive. Thus, learning from imperfect annotations (e.g. noisy or weak annotations) has emerged as a prominent research area in medical image segmentation. This work aims to extract high-quality pseudo masks from imperfect annotations with the assistance of a small number of clean labels. Our core motivation is based on the understanding that different types of flawed imperfect annotations inherently exhibit unique noise patterns. Comparing clean annotations with corresponding imperfectly annotated labels can effectively identify potential noise patterns at minimal additional cost. To this end, we propose a two-phase framework including a noise identification network and a noise-robust segmentation network. The former network implicitly learns noise patterns and revises labels accordingly. It includes a three-branch network to identify different types of noises. The latter one further mitigates the negative influence of residual annotation noises based on parallel segmentation networks with different initializations and a label softening strategy. Extensive experimental results on two public datasets demonstrate that our method can effectively refine annotation flaws and achieve superior segmentation performance to the state-of-the-art methods.
深度学习网络在图像分割方面表现出色,可以获得大量准确标注的训练样本。然而,在医疗应用中,获取大量高质量标注图像的成本过高。因此,从不完全性注释(如噪声或弱注释)中学习已成为医学图像分割的一个突出研究领域。这项工作旨在借助少量干净的标签,从不完善的注释中提取高质量的伪掩码。我们的核心动机是基于这样一种认识,即不同类型的有缺陷的不完善注释本质上表现出独特的噪声模式。将干净的注释与相应的不完美注释标签进行比较,能以最小的额外成本有效识别潜在的噪声模式。为此,我们提出了一个两阶段框架,包括一个噪声识别网络和一个噪声抑制分割网络。前一个网络隐式地学习噪声模式,并相应地修改标签。它包括一个三分支网络,用于识别不同类型的噪声。后一种网络基于具有不同初始化和标签软化策略的并行分割网络,进一步减轻了残余注释噪声的负面影响。在两个公共数据集上的大量实验结果表明,我们的方法可以有效地完善注释缺陷,并实现优于最先进方法的分割性能。
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引用次数: 0
Offline reward shaping with scaling human preference feedback for deep reinforcement learning 为深度强化学习提供具有规模化人类偏好反馈的离线奖励塑造。
IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-11-01 DOI: 10.1016/j.neunet.2024.106848
Jinfeng Li, Biao Luo, Xiaodong Xu, Tingwen Huang
Designing reward functions that fully align with human intent is often challenging. Preference-based Reinforcement Learning (PbRL) provides a framework where humans can select preferred segments through pairwise comparisons of behavior trajectory segments, facilitating reward function learning. However, existing methods collect non-dynamic preferences and struggle to provide accurate information about preference intensity. We propose scaling preference (SP) feedback method and qualitative and quantitative scaling preference (Q2SP) feedback method, which allow humans to express the true degree of preference between trajectories, thus helping reward learn more accurate human preferences from offline data. Our key insight is that more detailed feedback facilitates the learning of reward functions that better align with human intent. Experiments demonstrate that, across a range of control and robotic benchmark tasks, our methods are highly competitive compared to baselines and state of the art approaches.
设计完全符合人类意图的奖励功能往往具有挑战性。基于偏好的强化学习(PbRL)提供了一个框架,人类可以通过对行为轨迹片段进行成对比较来选择偏好的片段,从而促进奖励函数的学习。然而,现有方法收集的是非动态偏好,难以提供有关偏好强度的准确信息。我们提出了比例偏好(SP)反馈法和定性定量比例偏好(Q2SP)反馈法,它们允许人类表达轨迹之间的真实偏好程度,从而帮助奖励从离线数据中学习到更准确的人类偏好。我们的主要见解是,更详细的反馈有助于学习更符合人类意图的奖励函数。实验证明,在一系列控制和机器人基准任务中,我们的方法与基线和最先进的方法相比具有很强的竞争力。
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引用次数: 0
A temporal–spectral fusion transformer with subject-specific adapter for enhancing RSVP-BCI decoding 用于增强 RSVP-BCI 解码的带有特定对象适配器的时间-光谱融合转换器。
IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-11-01 DOI: 10.1016/j.neunet.2024.106844
Xujin Li , Wei Wei , Shuang Qiu , Huiguang He
The Rapid Serial Visual Presentation (RSVP)-based Brain–Computer Interface (BCI) is an efficient technology for target retrieval using electroencephalography (EEG) signals. The performance improvement of traditional decoding methods relies on a substantial amount of training data from new test subjects, which increases preparation time for BCI systems. Several studies introduce data from existing subjects to reduce the dependence of performance improvement on data from new subjects, but their optimization strategy based on adversarial learning with extensive data increases training time during the preparation procedure. Moreover, most previous methods only focus on the single-view information of EEG signals, but ignore the information from other views which may further improve performance. To enhance decoding performance while reducing preparation time, we propose a Temporal-Spectral fusion transformer with Subject-specific Adapter (TSformer-SA). Specifically, a cross-view interaction module is proposed to facilitate information transfer and extract common representations across two-view features extracted from EEG temporal signals and spectrogram images. Then, an attention-based fusion module fuses the features of two views to obtain comprehensive discriminative features for classification. Furthermore, a multi-view consistency loss is proposed to maximize the feature similarity between two views of the same EEG signal. Finally, we propose a subject-specific adapter to rapidly transfer the knowledge of the model trained on data from existing subjects to decode data from new subjects. Experimental results show that TSformer-SA significantly outperforms comparison methods and achieves outstanding performance with limited training data from new subjects. This facilitates efficient decoding and rapid deployment of BCI systems in practical use.
基于快速序列视觉呈现(RSVP)的脑机接口(BCI)是一种利用脑电图(EEG)信号进行目标检索的高效技术。传统解码方法的性能提升依赖于来自新测试对象的大量训练数据,这增加了 BCI 系统的准备时间。有几项研究引入了现有受试者的数据,以减少性能提升对新受试者数据的依赖,但其基于对抗学习的优化策略需要大量数据,这增加了准备过程中的训练时间。此外,之前的大多数方法只关注脑电信号的单视角信息,而忽略了其他视角的信息,而这些信息可能会进一步提高性能。为了在提高解码性能的同时减少准备时间,我们提出了一种带有特定对象适配器(Subject-specific Adapter,TSformer-SA)的时频谱融合转换器(Temporal-Spectral fusion transformer)。具体来说,我们提出了一个跨视图交互模块,以促进信息传递,并提取从脑电时间信号和频谱图图像中提取的双视图特征的共同表征。然后,基于注意力的融合模块会融合两个视图的特征,从而获得用于分类的综合判别特征。此外,我们还提出了多视图一致性损失,以最大限度地提高同一脑电信号的两个视图之间的特征相似性。最后,我们还提出了一种针对特定受试者的适配器,可将在现有受试者数据上训练的模型知识快速转移到新受试者数据的解码上。实验结果表明,TSformer-SA 的性能明显优于对比方法,并且在新受试者训练数据有限的情况下也能取得出色的表现。这有助于在实际应用中高效解码和快速部署生物识别(BCI)系统。
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引用次数: 0
Negative-Free Self-Supervised Gaussian Embedding of Graphs 图的无负自监督高斯嵌入。
IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-31 DOI: 10.1016/j.neunet.2024.106846
Yunhui Liu, Tieke He, Tao Zheng, Jianhua Zhao
Graph Contrastive Learning (GCL) has recently emerged as a promising graph self-supervised learning framework for learning discriminative node representations without labels. The widely adopted objective function of GCL benefits from two key properties: alignment and uniformity, which align representations of positive node pairs while uniformly distributing all representations on the hypersphere. The uniformity property plays a critical role in preventing representation collapse and is achieved by pushing apart augmented views of different nodes (negative pairs). As such, existing GCL methods inherently rely on increasing the quantity and quality of negative samples, resulting in heavy computational demands, memory overhead, and potential class collision issues. In this study, we propose a negative-free objective to achieve uniformity, inspired by the fact that points distributed according to a normalized isotropic Gaussian are uniformly spread across the unit hypersphere. Therefore, we can minimize the distance between the distribution of learned representations and the isotropic Gaussian distribution to promote the uniformity of node representations. Our method also distinguishes itself from other approaches by eliminating the need for a parameterized mutual information estimator, an additional projector, asymmetric structures, and, crucially, negative samples. Extensive experiments over seven graph benchmarks demonstrate that our proposal achieves competitive performance with fewer parameters, shorter training times, and lower memory consumption compared to existing GCL methods.
图对比学习(GCL)是最近出现的一种有前途的图自监督学习框架,用于学习无标签的判别节点表征。GCL 广泛采用的目标函数得益于两个关键特性:对齐性和均匀性,这两个特性可以对齐正节点对的表征,同时将所有表征均匀分布在超球面上。均匀性在防止表征坍塌方面起着至关重要的作用,它是通过将不同节点(负节点对)的增强视图推开来实现的。因此,现有的 GCL 方法本质上依赖于增加负样本的数量和质量,从而导致了繁重的计算需求、内存开销和潜在的类碰撞问题。在本研究中,我们提出了一种无负目标来实现均匀性,其灵感来自于根据归一化各向同性高斯分布的点在单位超球面上均匀分布这一事实。因此,我们可以最小化所学表征分布与各向同性高斯分布之间的距离,以促进节点表征的均匀性。我们的方法还有别于其他方法,它不需要参数化的互信息估计器、额外的投影器、非对称结构,更重要的是不需要负样本。在七个图基准上进行的广泛实验表明,与现有的 GCL 方法相比,我们的建议以更少的参数、更短的训练时间和更低的内存消耗实现了具有竞争力的性能。
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引用次数: 0
A general adaptive unsupervised feature selection with auto-weighting 具有自动加权功能的通用自适应无监督特征选择。
IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-31 DOI: 10.1016/j.neunet.2024.106840
Huming Liao , Hongmei Chen , Tengyu Yin , Zhong Yuan , Shi-Jinn Horng , Tianrui Li
Feature selection (FS) is essential in machine learning and data mining as it makes handling high-dimensional data more efficient and reliable. More attention has been paid to unsupervised feature selection (UFS) due to the extra resources required to obtain labels for data in the real world. Most of the existing embedded UFS utilize a sparse projection matrix for FS. However, this may introduce additional regularization terms, and it is difficult to control the sparsity of the projection matrix well. Moreover, such methods may seriously destroy the original feature structure in the embedding space. Instead, avoiding projecting the original data into the low-dimensional embedding space and identifying features directly from the raw features that perform well in the process of making the data show a distinct cluster structure is a feasible solution. Inspired by this, this paper proposes a model called A General Adaptive Unsupervised Feature Selection with Auto-weighting (GAWFS), which utilizes two techniques, non-negative matrix factorization, and adaptive graph learning, to simulate the process of dividing data into clusters, and identifies the features that are most discriminative in the clustering process by a feature weighting matrix Θ. Since the weighting matrix is sparse, it also plays the role of FS or a filter. Finally, experiments comparing GAWFS with several state-of-the-art UFS methods on synthetic datasets and real-world datasets are conducted, and the results demonstrate the superiority of the GAWFS.
特征选择(FS)在机器学习和数据挖掘中至关重要,因为它能使高维数据的处理更高效、更可靠。由于在现实世界中获取数据标签需要额外资源,无监督特征选择(UFS)受到了更多关注。现有的大多数嵌入式 UFS 都利用稀疏投影矩阵进行特征选择。然而,这可能会引入额外的正则化项,而且很难很好地控制投影矩阵的稀疏性。此外,这种方法可能会严重破坏嵌入空间中的原始特征结构。相反,避免将原始数据投影到低维嵌入空间,直接从原始特征中识别出在使数据显示出明显聚类结构过程中表现良好的特征,不失为一种可行的解决方案。受此启发,本文提出了一种名为 "带自动加权的通用自适应无监督特征选择"(General Adaptive Unsupervised Feature Selection with Auto-weighting, GAWFS)的模型,它利用非负矩阵因式分解和自适应图学习这两种技术来模拟将数据划分为聚类的过程,并通过特征加权矩阵Θ来识别聚类过程中最具区分度的特征。由于加权矩阵是稀疏的,因此它也起到了 FS 或过滤器的作用。最后,在合成数据集和实际数据集上对 GAWFS 和几种最先进的 UFS 方法进行了比较实验,结果证明了 GAWFS 的优越性。
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引用次数: 0
Data-sampled time-varying formation for singular multi-agent systems with multiple leaders 具有多个领导者的奇异多代理系统的数据采样时变形成。
IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-31 DOI: 10.1016/j.neunet.2024.106843
Fenglan Sun , Xuemei Yu , Wei Zhu , Jürgen Kurths
The time-varying formation problem of singular multi-agent systems under sampled data with multiple leaders is investigated in this paper. Firstly, a data-sampled time-varying formation control protocol is proposed in the current study where the communication among followers merely occurred at sampling instants, which can save the controller communication energy significantly. Secondly, necessary and sufficient conditions for the feasibility of the formation function are provided. In addition, an approach is presented to design the formation tracking control under sampled data with multiple leaders. Finally, numerical simulations validate the efficacy of the theoretical results.
本文研究了具有多个领导者的采样数据下奇异多代理系统的时变编队问题。首先,本文提出了一种数据采样时变编队控制协议,跟随者之间的通信仅发生在采样时刻,这可以大大节省控制器的通信能量。其次,提供了编队函数可行性的必要条件和充分条件。此外,还提出了一种方法来设计具有多个领导者的采样数据下的编队跟踪控制。最后,数值模拟验证了理论结果的有效性。
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引用次数: 0
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Neural Networks
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