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Despite significant advancements in multi-view multi-label learning driven by its broad applicability, real-world scenarios frequently suffer from dual incompleteness in both view and label spaces due to data acquisition uncertainties. The incompleteness of multi-view features degrades the comprehensiveness of sample representations, leading to failure in capturing semantically discriminative patterns essential for classification. To address the aforementioned challenges, we propose a novel learning framework termed V iew- L abel Driven C ross-Space S tructure A lignment Network (VLCSA). Departing from existing low-quality view completion approaches, we devise a view-label hybrid-driven autoencoder (VLAE) that extracts discriminative view-specific features through joint optimization of cross-view semantic consistency and label-guided instance-level embeddings, which allows for accurate reconstruction of missing views. Furthermore, we propose cross-space structure alignment (CSA), which imposes view-label hybrid-driven losses on both original and complete feature spaces to enforce structural consistency between partial and holistic semantic topologies. Recognizing the suboptimal data reconstruction quality during initial training phases, we propose phase-aware excitation (PAE) to mitigate error accumulation in early-stage learning. Additionally, we introduce star-shaped interactive sharing module (SIS) that facilitates efficient cross-view information exchange while leveraging view complementarity to ensure consistent and robust feature aggregation, circumventing conflicts with view-consistency alignment objectives. d on five widely-adopted benchmark datasets indicate that the proposed VLCSA framework outperforms numerous established baselines in terms of comprehensive evaluation metrics.

In this paper, we study the partial multi-label (PML) image classification problem, where each image is annotated with a candidate label set consisting of multiple relevant labels and other noisy labels. Existing PML methods typically design a disambiguation strategy to filter out noisy labels by utilizing prior knowledge with extra assumptions, which unfortunately is unavailable in many real tasks. Furthermore, because the objective function for disambiguation is usually elaborately designed on the whole training set, it can hardly be optimized in a deep model with stochastic gradient descent (SGD) on mini-batches. In this paper, for the first time, we propose a deep model for PML to enhance the representation and discrimination ability. On the one hand, we propose a novel curriculum-based disambiguation strategy to progressively identify ground-truth labels by incorporating the varied difficulties of different classes. On the other hand, consistency regularization is introduced for model training to balance fitting identified easy labels and exploiting potential relevant labels. Extensive experimental results on the commonly used benchmark datasets show that the proposed method significantly outperforms the SOTA methods.

Recently, multi-view multi-label learning has gained significant attention due to its applicability in various domains. However, due to the limitations of data collection and the subjectivity of manual labeling, multi-view multi-label learning often faces both partial views and incomplete labels, substantially impacting the performance of existing classification methods in practical applications. Although existing methods have attempted to address this issue, they struggle to fully exploit the consistency and complementarity of multi-view multi-label data simultaneously. To overcome this limitation, we propose a novel structure-guided decoupled contrastive framework (SGDC). Specifically, to address the limitations of conventional single-encoder paradigms, SGDC innovatively incorporates a decoupled disentanglement mechanism (DDM). By integrating a dual-encoder architecture with mutual information upper bound constraints, DDM decouples multi-view features into view-specific and view-consistent components, which can improve the quality of feature representations. Additionally, the SGDC integrates a structure-guided contrastive (SGC) framework that performs dynamic semantic alignment in consistent feature spaces through global structural modeling, effectively implementing structure-conscious representation learning guided by the multi-view consensus principle. Experimental results on multiple datasets consistently demonstrate the effectiveness of our method in handling complex multi-view multi-label data.

Estimation of individuals' cognitive, behavioral and demographic (CBD) variables based on MRI has attracted much research interest in the past decade, and effective machine learning techniques are of great importance for these estimations. Partial least squares regression (PLSR) is an attractive machine learning technique that can accommodate both single- and multi-label learning in a simple framework, while its potential for MRI-based estimations of CBD variables remains to be explored. In this study, we systemically investigated the performance of PLSR in MRI-based estimations of individuals' CBD variables, especially its performance in simultaneous estimation of multiple CBD variables (multi-label learning). We performed the study on the dataset included in the HCP S1200 release. Resting state functional connections (RSFCs) were used as features, and a total of 10 CBD variables (e.g., age, gender, grip strength, and picture vocabulary) were estimated. The results showed that PLSR performed well in both single- and multi-label learning. In fact, the present estimations were better than those reported in literatures, as indicated by stronger correlations between the estimated and actual CBD variables, as well as high gender classification accuracy (97.8% in this study). Moreover, the RSFCs that contributed to the estimations exhibited strong correlations with the CBD variable estimated, that is, PLSR algorithm automatically selected the RSFCs closely related to one CBD variable to establish predictive models for the variable. Besides, the estimation accuracies based on RSFCs among 100, 200, and 300 regions of interest (ROIs) were higher than those based on RSFCs among 15, 25, and 50 ROIs; the estimation accuracies based on RSFCs evaluated using partial correlation were higher than those based on RSFCs evaluated using full correlation. In addition to the aforementioned virtues, PLSR is efficient in model training and testing, and it is simple and easy to use. Therefore, PLSR can be a favorable choice for future MRI-based estimations of CBD variables.

In recent years, multi-view multi-label learning has garnered considerable attention due to its broad application prospects, such as bioinformatics and medical imaging. However, the integrity of multi-view and multi-label data cannot be guaranteed in practical scenarios. Currently, some frameworks have been introduced to address the complex issue of partial multi-view incomplete multi-label classification, but they frequently overlook the impact of view quality on the learning of semantic information. To tackle this problem, we propose a semantic consistency enhancement and contribution-driven network(SCECD-Net). Different from the existing works, we focus on the quality differences between views, dynamically adjusting the mutual information learning process by quantifying view quality to reduce the model’s reliance on noisy information and more effectively capture view consistency. Furthermore, considering that treating each view equally during the reconstruction process may limit the model’s ability to leverage useful information, we propose a contribution-driven reconstruction strategy, which captures the contributions of each view using reliable supervisory information and employs this to balance the reconstruction of views, selectively retaining the most critical information. Extensive evaluations conducted on five datasets indicate that our method performs better than other approaches.

Inthis paper, a distributed semi-supervised partial multi-label learning (dS2PML) algorithm is proposed, which can be used to address the problem of distributed classification of partially multi-labeled data and unlabeled data. In this algorithm, we utilize the multi-kernel function together with the label correlation term to construct the discriminant function. In addition, to obtain a decentralized implementation, we design a reconstructed error on the labeling confidence based on globally common basic data that are selected by a distributed strategy. By exploiting the similarity structure among feature and label spaces under the sparsity constraint, the labeling confidences of partially multi-labeled and unlabeled data are estimated in a decentralized manner. Meanwhile, by using the sparse random feature map to approximate the kernel feature map, the multi-label classifier can be trained under the supervision of the estimated labeling confidence. Experiments on multiple real datasets are conducted to evaluate the learning performance of the proposed approach. According to the experimental results, the average ranks of all the comparison algorithms evaluated on five evaluation metrics are computed. The ranking results show that the average ranks of our algorithm in terms of hamming loss, one error, average precision, ranking loss, and coverage are 3.16, 2.27, 2.15, 2.38, and 2.18, respectively. The average ranks of the dS2PML are second only to the corresponding centralized S2PML (cS2PML) algorithms and higher than other existing comparison algorithms in five evaluation metrics. The average rank differences in terms of Hamming loss, one error, average precision, ranking loss, and coverage between our proposed algorithm and the closest comparison algorithm are 0.28, 1.67, 1.80, 1.15, and 1.62, respectively. Additionally, owing to the distributed storage and decentralized processing of training data, our proposed dS2PML algorithm reduces CPU time by more than 65% and memory consumption by more than 6% compared to the centralized comparison algorithms. The experimental results indicate that our proposed algorithm outperforms the other state-of-the-art algorithms in classification accuracy, CPU time, and memory consumption.

We present a neural-network-based approach to solve the image-to-image translation problem in microscale droplets squeeze flow. A residual convolutional neural network is proposed to address the inverse problem: reconstructing a low-resolution (LR) droplet pattern image from a high-resolution (HR) liquid film thickness imprint. This enables the prediction of initial droplet configurations that evolve into target HR imprints after a specified spreading time. The developed neural network architecture aims at learning to tune the refinement level of its residual convolutional blocks by using function approximators that are trained to map a given film thickness to an appropriate refinement level indicator. We use multiple stacks of convolutional layers, the output of which is translated according to the refinement level indicators provided by the directly connected function approximators. Together with a non-linear activation function, the translation mechanism enables the HR imprint image to be refined sequentially in multiple steps until the target LR droplet pattern image is revealed. We believe that this work holds value for the semiconductor manufacturing and packaging industry. Specifically, it enables desired layouts to be imprinted on a surface by squeezing strategically placed droplets with a blank surface, eliminating the need for customized templates and reducing manufacturing costs. Additionally, this approach has potential applications in data compression and encryption.

Presently, data that are collected from real systems and organized as information networks are universal. Mining hidden information from these data is generally helpful to understand and benefit the corresponding systems. The challenges of analyzing such data include high computational complexity and low parallelizability because of the nature of complicated interconnected structure of their nodes. Network representation learning, also called network embedding, provides a practical and promising way to solve these issues. One of the foremost requirements of network embedding is preserving network topology properties in learned low-dimension representations. Community structure is a prominent characteristic of complex networks and thus should be well maintained. However, the difficulty lies in the fact that the properties of community structure are multivariate and complicated; therefore, it is insufficient to model community structure using a predefined model, the way that is popular in most state-of-the-art network embedding algorithms explicitly considering community structure preservation. In this paper, we introduce a multi-process parallel framework for network embedding that is enhanced by found partial community information and can preserve community properties well. We also implement the framework and propose two node embedding methods that use game theory for detecting partial community information. A series of experiments are conducted to evaluate the performance of our methods and six state-of-the-art algorithms. The results demonstrate that our methods can effectively preserve community properties of networks in their low-dimension representations. Specifically, compared to the involved baselines, our algorithms behave the best and are the runners-up on networks with high overlapping diversity and density.

In reality, data objects often belong to several different categories simultaneously, which are semantically correlated to each other. Multi-label learning can handle and extract useful information from such kind of data effectively. Since it has a great variety of potential applications, multi-label learning has attracted widespread attention from many domains. However, two major challenges still remain for multi-label learning: high dimensionality and correlations of data. In this paper, we address the problems by using the technique of partial least squares (PLS) and propose a new multi-label learning method called rPLSML (regularized Partial Least Squares for Multi-label Learning). Specifically, we exploit PLS discriminant analysis to identify a latent and common space from the variable and label spaces of data, and then construct a learning model based on the latent space. To tackle the multi-collinearity problem raised from the high dimensionality, a ℓ 2 -norm penalty is further exerted on the optimization problem. The experimental results on public data sets show that rPLSML has better performance than the state-of-the-art multi-label learning algorithms.