Yu Lei

目的高光谱图像具有丰富的光谱信息，能够为低空场景下地物的精细分类提供重要支撑，但其高维特性也带来了较大的存储与计算负担。因此，在低空平台载荷受限且标记样本稀缺的条件下，获取低维但表示能力优秀的波段子集成为一个值得研究的方向。现有半监督波段选择方法通常仅依赖单一数据集中的标记与未标记样本，受标记样本不足的影响，所获得的波段子集判别能力有限。由于同一传感器获取的高光谱图像具有相似的反射和辐射特性，协同分析多个高光谱数据集能够有效缓解单一数据集监督信息不足的问题。方法本文提出一种进化多任务优化的多数据集协同波段选择方法（Evolutionary Multitasking Optimization-based Multi-dataset Collaborative Band Selection， EMDCBS）。该算法为每个数据集建立了各自的私有无监督波段选择任务，充分挖掘各个数据集具有丰富地物信息的波段子集。此外，设计了一个共享监督波段选择任务，同时利用多数据集的标记样本缓解了标记样本不足的问题，获取判别能力强的波段子集。在优化过程中，借助进化多任务优化机制实现不同任务之间的知识迁移与协同搜索，其中私有无监督任务以信息熵为目标，共享监督任务以类间散度与类内散度构成的可分性准则为目标。进一步地，针对不同任务获得的多样化最优波段子集，设计多波段子集融合策略，从信息量、判别性和冗余性三个方面进行综合筛选，最终为各数据集生成综合性能更优的波段子集。结果在 PaviaU、PaviaC、LongKou 和 HongHu 4 个公开高光谱数据集上的实验表明，在每类仅使用 50 个标记样本、波段子集大小设置为 20 的条件下，所提方法取得了优于对比波段选择方法的综合分类性能。其总体分类精度（OA）分别达到 90.05%、95.89%、93.39% 和 79.21%。此外，实验部分还展示了各种参数对实验结果的影响，对算法进行了收敛性分析，并验证了所提出策略的有效性。结论所提 EMDCBS 方法能够充分挖掘并利用同一传感器多数据集之间潜在的公共光谱知识，在有限标记样本条件下实现更高效、更具判别性的波段选择。通过联合私有无监督任务与共享监督任务，并结合进化多任务优化和子集融合策略，该方法有效提升了任务优化效率和最优波段子集性能。

Band selection (BS) is commonly utilized to reduce the dimensionality of hyperspectral images (HSI). Some semi-supervised BS methods achieve great classification performance using limited labeled samples. However, most methods rely on a unified discriminant learner to find the band subset, which lacks consideration for the complex feature representation of HSI data. This paper proposes a semi-supervised adaptive BS method based on evolutionary multitasking to effectively explore the diverse intrinsic characteristics. The BS problem is modeled as a multitask optimization problem where multiple BS tasks jointly select optimal band subset based on different discriminant learners. To achieve the collaborative search of these BS tasks, a structure-aware evolutionary multitasking approach is proposed, which generates the individuals with various number of bands by considering the low redundancy and complete spectral structure information, and leverages the specially designed genetic operations to facilitate the population evolution under the structure preservation constraint. A dynamic interaction mechanism is devised to adjust task interaction intensity adaptively to enhance effective knowledge sharing between tasks. Additionally, a discriminability evaluation criterion is proposed to mitigate noisy sample interference by integrating sample reliability and class separability. Experiments have demonstrated that the proposed method effectively explores the complex feature representation of HSI data through joint discriminant learners.

In recent years, deep learning (DL) has achieved remarkable success in classifying hyperspectral images (HSIs), relying heavily on the quantity and quality of labeled samples. However, obtaining sufficient labels for HSIs poses a challenge. HSIs obtained by the same sensor often exhibit similar spectral information due to their shared physical, chemical properties, or reflective attributes. Joint analysis of several HSIs enables the integration of limited labeled samples and extraction of more robust and discriminative features from different HSIs. Therefore, a multitask collaborative network (MTCN) for the joint classification of multiple HSIs acquired by the same sensor in different areas is proposed. In the MTCN, each HSI has its own feature extraction channel, which facilitates the learning of image-specific representations. In addition, a feature sharing channel (FSC) is created to extract and transfer multihierarchical image-shared representations between multiple HSIs, thereby forming a common knowledge pool to facilitate feature sharing. Furthermore, a cross-channel mutual attention module (CMAM) is designed to collaboratively utilize features from image-specific and image-shared channels, enhancing the efficiency of information communication in HSIs. The experimental results on six HSIs demonstrate that the proposed MTCN can jointly classify multiple HSIs by the same sensor in different areas and achieve good classification performance.

Unsupervised domain adaptation (UDA) is widely utilized to accomplish the visual object recognition task in the field of consumer electronics, aiming at reducing labeling costs in new scenarios. To address the error accumulation problem caused by unreliable target pseudo labels, existing feature-based UDA methods utilize the predicted posterior probability of target sample to model the uncertainty relationship between samples. However, due to differences in data characteristics of each class, unified uncertainty modeling may lead to information loss in some classes due to excessive fuzziness. In view of this, this paper proposes a uncertainty feature learning method with personalized class description for UDA (UFL-PCD) to accomplish the visual object recognition task. Firstly, to learn a more robust discriminative common feature subspace, the proposed method performs more flexible uncertainty modeling of relationships between intra-domain and cross-domain samples based on the personalized uncertainty description of each class. Secondly, to alleviate the error accumulation problem, a hybrid label filtering strategy is proposed to determine the credible target pseudo labels based on fuzzy membership degrees and adaptive rough approximations. Furthermore, a consensus prototype-driven prediction model is designed to predict target data by comprehensively considering the guidance information of the source domain and the intrinsic structural information of the target domain. Experiments conducted on four benchmark datasets have demonstrated that UFL-PCD can construct a common feature subspace with strong stability and adaptability through uncertainty feature learning.

To address the problem of enormous differences in two heterogeneous images, the traditional unsupervised frameworks are most normally realized by converting two images into a common domain with various auxiliary strategies, such as transformation and alignment, which requires extensive calculation and has difficulty balancing the training tasks. To achieve a concise framework, this paper proposes self-guided autoencoders (SGAE) for unsupervised change detection in heterogeneous remote sensing images. Unlike traditional methods that aim to narrow the differences of heterogeneous images to highlight the changed information, SGAE forces the flow of identification in formation generated in unlabeled data through self-guided iterations. First, initial unsupervised networks output an elementary change map that will be screened to obtain reliable pseudolabels. The selected pseudo-labeled samples will be used as the input of a supervised network to obtain another change map. Then, multiple change maps will be fused to refine the confidence of pseudolabels again, obtaining new fused pseudolabeled samples for the self-guided network, which will be trained with pseudo-labeled samples and unlabeled samples. Finally, the above operations will be repeated to continuously optimize the net, which helps itself to extract the discriminative features for classification in self-guided iterations. Experiments compared with several algorithms on four datasets demonstrate the effectiveness and robustness of our method, which can help unsupervised models improve discriminative feature extraction and classification performance with a more flexible learning method.

In the era of burgeoning AI technology and progressive educational systems, AI’s influence on education has become increasingly apparent. The intricacies between courses continue to intensify, heightening the need for researchers to conduct precise correlation analyses. Traditional practices, which rely solely on correlation coefficients, tend to focus on linear relationships between two courses, leaving much to be desired. To delve deeper into the relationships between multiple courses, a growing number of AI-assisted methodologies have emerged in the education arena. Consequently, we introduce the Multilayer Perceptron (MLP) course relation approach, which scrutinizes the direct relationships embedded within grade data. Empirical evidence highlights the proposed MLP course correlation method’s capacity to reveal nonlinear and profound associations across multiple courses, showcasing its potential to revolutionize course correlation analysis.

With the progress of the times and the development of technology, the creation of artificial intelligence technology has brought a huge impact on the field of education. For the field of education, exploring curriculum relevance is one of its core elements. However, most of the traditional course relevance analysis is conducted from the perspective of course performance, ignoring the external factors that affect the performance such as test difficulty and student level. Since course text descriptions can describe a course more objectively than grades, it is necessary to apply text similarity analysis to course relevance research. The prevalent text similarity analysis is only achieved by word frequency statistics, ignoring the connection between textual contexts. In this context, this paper utilizes the Word Mover’s Distance (WMD) approach to analyze the relevance of course texts. The experimental results show the heat map of course relevance and demonstrate the superiority and effectiveness of the WMD method through the experimental comparison with the traditional text distance metric algorithm.

The widespread dissemination of fake news across the internet has profound detrimental consequences for society, governments, and citizens. To address this pressing issue, numerous machine learning-based models have been developed for detecting fake news. However, the challenge of acquiring sufficient labeled news data in a new domain, coupled with the presence of inconsistent data distribution, necessitates the integration of unsupervised domain adaptation (DA) methods to enhance the reliability of cross-domain fake news detection. In this article, a rough-fuzzy graph learning DA for fake news detection is proposed. First, a rough-fuzzy graph learning method is proposed to effectively handle the representation of cross-domain sample uncertainty structural information, thereby learning a more discriminative subspace. Second, a rough-fuzzy region division strategy is designed to perform different analysis on target domain samples, thus achieving a more accurate description of the relationships between cross-domain samples. Furthermore, considering that domain private features may negatively affect the knowledge transfer process, a sparse structure preserving strategy is proposed to better capture shared general features across domains. Experimental evaluations conducted on three news datasets demonstrate the efficacy of the proposed method in cross-domain fake news detection.