Brain decoding currently faces significant challenges in individual differences, modality alignment, and high-dimensional embeddings. To address individual differences, researchers often use source subject data, which leads to issues such as privacy leakage and heavy data storage burdens. In modality alignment, current works focus on aligning the softmax probability distribution but neglect the alignment of marginal probability distributions, resulting in modality misalignment. Additionally, images and text are aligned separately with fMRI without considering the complex interplay between images and text, leading to poor image reconstruction. Finally, the enormous dimensionality of CLIP embeddings causes significant computational costs. Although the dimensionality of CLIP embeddings can be reduced by ignoring the number of patches obtained from images and the number of tokens acquired from text, this comes at the cost of a significant drop in model performance, creating a dilemma. To overcome these limitations, we propose a source-free domain adaptation-based brain decoding framework. Firstly, we apply source-free domain adaptation, which only acquires the source model without accessing source data during target model adaptation, to brain decoding to address cross-subject variations, privacy concerns, and the heavy burden of data storage. Secondly, we employ maximum mean discrepancy (MMD) to align the marginal probability distributions between embeddings of different modalities. Moreover, to accommodate the complex interplay between image and text, we concatenate the embeddings of image and text and then use singular value decomposition (SVD) to obtain a new embedding. What’s more, to achieve better image generation quality, we employ the Wasserstein distance (WD) to align the probability distributions of new embeddings. Finally, in the target model adaptation phase of source-free domain adaptation, we employ low-rank adaptation (LoRA) to reduce the high expense of tuning the target model. Sufficient experiments demonstrate our work outperforms state-of-the-art methods for brain decoding tasks.

Benefiting from the gap increasing between the optimal action and its competitors, the advantage learning (AL) operator is more robust to estimation errors in the approximated $Q$ -functions than the Bellman optimality operator in reinforcement learning (RL). However, our analysis reveals that its robustness and larger action gaps come at the cost of a worse performance loss bound, leading to slower convergence of value functions. To address this issue, we present a novel method, named Occam's Razor-based AL (ORAL), which follows Occam's Razor principle and takes the necessity into consideration when increasing the action gap. Specifically, our ORAL can adaptively increase the action gap for different state-action pairs, depending on the proximity of their $Q$ values to the optimal ones. We first propose a naive implementation of ORAL, employing a nonsmooth clipping function to realize the above idea, and then introduce a smooth version of ORAL aimed at achieving more stable learning. Furthermore, our methods can be easily plugged into other AL-based operators and extended to more complex continuous-control tasks. Theoretical analysis supports the feasibility of our approaches, demonstrating their ability to balance the gap increasing with fast convergence. Empirical results further validate its effectiveness, showing significant performance improvements across multiple benchmarks.

For electroencephalogram (EEG)-based emotion recognition, various EEG features are extracted from frequency, time, and time-frequency domains for modeling. Nevertheless, there is no a standard subset of EEG features widely accepted in this research field, giving rise to the challenge of curse dimensionality. To cope with the challenge, many EEG feature selection (FS) methods have been put forward based on information theory. Generally, these methods suffer from the issue of delivering a suboptimal result with heuristic search, and they are also inefficient in balancing the influence of different terms like feature relevance and feature redundancy. Based on this, we present a new fuzzy information-theoretic based optimization approach to attain the goal. To be specific, fuzzy mutual information is unitized to evaluate EEG features from the relevance and redundancy perspective, and data structure information is captured to exploit feature manifold simultaneously. Then, a unified optimization framework is designed to take all of them into consideration, thereby inducing a globally optimal result of EEG FS. Extensive empirical studies on three EEG emotional datasets reveal that our method is able to found out a discriminative and non-redundant feature subset from different domains, and therefore achieves the performance improvement of emotion recognition.

Semi-supervised multi-label learning (SSMLL) involves learning a multi-label classifier from a small set of labeled data and a large set of unlabeled data. Label enhancement (LE), accounting for the relative importance of labels, has been effective in improving the performance of supervised multi-label learning models. Nevertheless, generating a robust SSMLL model with LE based on incomplete label information remains challenging. In this paper, we pioneer the idea of applying LE to SSMLL. First, we design a kNN aggregation-based method, aiming to assign pseudo-labels to unlabeled data and perform the LE process by aggregating label information from neighboring instances. Leveraging the topological structure of the feature space is an effective LE approach for training. However, LE, decoupled from the training process, lacks the dynamic feedback of the training model. To improve this, we incorporate a label propagation mechanism that iteratively optimizes the LE process with the guidance of the available label information. Moreover, we consider local label correlations according to local linear embedding to further enhance the generalization ability of the learning model. Extensive experiments demonstrate that the proposed approach can effectively recover latent label information, resulting in significant performance improvement in SSMLL.

In recommendation systems, cold-start user recommendation is a challenging problem, where precise recommendations are required for users who have not appeared before. Several existing cold-start user recommendation models adopt domain adaptation to extract information from auxiliary source domains to assist the recommendations on the target domain. In this article, we propose that the cold-start user recommendation problem can be formulated by the heterogeneous domain adaption approach. We determine a transformation of user features, e.g., user social relations and historical interactions between warm users and their interested items, into a latent space so that the loss function is set by user feature reconstruction and by feature and distribution matching in the heterogeneous domains. The resulting optimization problem can be solved by matrix eigendecomposition, and the cold-start users’ preferences can thus be obtained. We also extend the proposed model using neural networks. We perform extensive experiments on several real-world datasets, and the results in terms of Precision, Recall, NDCG, and Hit Rate verify the effectiveness of the proposed model.

Graph-structured data, where nodes exhibit either pair-wise or high-order relations, are ubiquitous and essential in graph learning. Despite the great achievement made by existing graph learning models, these models use the direct information (edges or hyperedges) from graphs and do not adopt the underlying indirect information (hidden pair-wise or high-order relations). To address this issue, in this paper, we propose a general framework named Simplicial Complex Neural (SCN) network, in which we construct a simplicial complex based on the direct and indirect graph information from a graph so that all information can be employed in the complex network learning. Specifically, we learn representations of simplices by aggregating and integrating information from all the simplices together via layer-by-layer simplicial complex propagation. In consequence, the representations of nodes, edges, and other high-order simplices are obtained simultaneously and can be used for learning purposes. By making use of block matrix properties, we derive the theoretical bound of the simplicial complex filter learnt by the propagation and establish the generalization error bound of the proposed simplicial complex network. We perform extensive experiments on node (0-simplex), edge (1-simplex), and triangle (2-simplex) classifications, and promising results demonstrate the performance of the proposed method is better than that of existing graph and hypergraph network approaches.

Information theoretical-based methods have attracted a great attention in recent years and gained promising results for multilabel feature selection (MLFS). Nevertheless, most of the existing methods consider a heuristic way to the grid search of important features, and they may also suffer from the issue of fully utilizing labeling information. Thus, they are probable to deliver a suboptimal result with heavy computational burden. In this article, we propose a general optimization framework global relevance and redundancy optimization (GRRO) to solve the learning problem. The main technical contribution in GRRO is a formulation for MLFS while feature relevance, label relevance (i.e., label correlation), and feature redundancy are taken into account, which can avoid repetitive entropy calculations to obtain a global optimal solution efficiently. To further improve the efficiency, we extend GRRO to filter out inessential labels and features, thus facilitating fast MLFS. We call the extension as GRROfast, in which the key insights are twofold: 1) promising labels and related relevant features are investigated to reduce ineffective calculations in terms of features, even labels and 2) the framework of GRRO is reconstructed to generate the optimal result with an ensemble. Moreover, our proposed algorithms have an excellent mechanism for exploiting the inherent properties of multilabel data; specifically, we provide a formulation to enhance the proposal with label-specific features. Extensive experiments clearly reveal the effectiveness and efficiency of our proposed algorithms.

In recommendation systems, the cold-start issue is a long-standing problem where no historical interaction records are given for certain users or items. Under this circumstance, recommendations for new users or new items become challenging. To address this problem, most existing approaches seek to discover a latent common space for users and items. However, these methods require a strong assumption that a shared space exists where the distributions of users and items are identical, which may limit the recommendation performance. In this article, we propose a novel model called Feature Matching Machine (FMM) to learn latent informative user and item representations. Different from previous methods, for warm users (or items), FMM learns two kinds of latent features, i.e., one is constructed by a hypergraph auto-encoder based on historical interactions between users and items, and the other is built by a multi-layer perceptron based on users (or items). Subsequently, FMM matches these two latent feature representations so as to discover the relationships across users (or items) and cold-start items (or users). We conduct extensive experiments on several real-world datasets and compare the proposed method with well-known baseline methods. Promising results demonstrate the effectiveness and efficiency of the proposed model.

Class imbalance is a common issue in the community of machine learning and data mining. The class-imbalance distribution can make most classical classification algorithms neglect the significance of the minority class and tend toward the majority class. In this article, we propose a label enhancement method to solve the class-imbalance problem in a graph manner, which estimates the numerical label and trains the inductive model simultaneously. It gives a new perspective on the class-imbalance learning based on the numerical label rather than the original logical label. We also present an iterative optimization algorithm and analyze the computation complexity and its convergence. To demonstrate the superiority of the proposed method, several single-label and multilabel datasets are applied in the experiments. The experimental results show that the proposed method achieves a promising performance and outperforms some state-of-the-art single-label and multilabel class-imbalance learning methods.

Recommendation systems provide personalized service to users and aim at suggesting to them items that they may prefer. There is an increasing requirement of next-item recommendation systems to infer a user's next favor item based on his/her historical selection of items. In this article, we study the next-item recommendation under the cold-start situation, where the users in the system share no interaction with the new items. Specifically, we seek to address the problem from the perspective of zero-shot learning (ZSL), which classifies samples whose classes are unseen during training. To this end, we crystallize the relationship and setting from ZSL to cold-start next-item recommendation, and further propose a novel model called User-Item Matching and Auto-encoders (UIMA) which learns the latent embeddings for both users and items by exploiting user historical preferences and item attributes. Concretely, UIMA consists of three components, i.e., two auto-encoders for learning user and item embeddings and a matching network to explore the relationship between the learned user and item embeddings. We perform experiments on several cold-start next-item recommendation datasets, including movies, music, and bookmarks. Promising results demonstrate the effectiveness of the proposed method for cold-start next-item recommendation.

In multilabel learning, each training example is represented by a single instance, which is relevant to multiple class labels simultaneously. Generally, all relevant labels are considered to be available for labeled data. However, instances with a full label set are difficult to obtain in real-world applications, thus leading to the weakly multilabel learning problem, that is, relevant labels of training data are partially known and many relevant labels are missing, and even abundant training data are associated with an empty label set. To address the problem, we propose a new multilabel method to learn from weakly labeled data. To be specific, an optimization framework is constructed based on the manifold regularized sparse model, in which the correlations among labels and feature structure are considered to model global and local label correlations, thereby achieving discriminative feature analysis for mapping training data to ground-truth label space. Moreover, the proposed method has an excellent mechanism to conduct semisupervised multilabel learning by exploiting training data with the predicted label set of the unlabeled. Experiments on various real-world tasks reveal that the proposed method outperforms some state-of-the-art methods.

As a group of complex neurodevelopmental disorders, autism spectrum disorder (ASD) has been reported to have a high overall prevalence, showing an unprecedented spurt since 2000. Due to the unclear pathomechanism of ASD, it is challenging to diagnose individuals with ASD merely based on clinical observations. Without additional support of biochemical markers, the difficulty of diagnosis could impact therapeutic decisions and, therefore, lead to delayed treatments. Recently, accumulating evidence have shown that both genetic abnormalities and chemical toxicants play important roles in the onset of ASD. In this work, a new multilabel classification (MLC) model is proposed to identify the autistic risk genes and toxic chemicals on a large-scale data set. We first construct the feature matrices and partially labeled networks for autistic risk genes and toxic chemicals from multiple heterogeneous biological databases. Based on both global and local measure metrics, the simulation experiments demonstrate that the proposed model achieves superior classification performance in comparison with the other state-of-the-art MLC methods. Through manual validation with existing studies, 60% and 50% out of the top-20 predicted risk genes are confirmed to have associations with ASD and autistic disorder, respectively. To the best of our knowledge, this is the first computational tool to identify ASD-related risk genes and toxic chemicals, which could lead to better therapeutic decisions of ASD.

Information theoretical based methods have attracted a great attention in recent years, and gained promising results to deal with multi-label data with high dimensionality. However, most of the existing methods are either directly transformed from heuristic single-label feature selection methods or inefficient in exploiting labeling information. Thus, they may not be able to get an optimal feature selection result shared by multiple labels. In this paper, we propose a general global optimization framework, in which feature relevance, label relevance (i.e., label correlation), and feature redundancy are taken into account, thus facilitating multi-label feature selection. Moreover, the proposed method has an excellent mechanism for utilizing inherent properties of multi-label learning. Specially, we provide a formulation to extend the proposed method with label-specific features. Empirical studies on twenty multi-label data sets reveal the effectiveness and efficiency of the proposed method. Our implementation of the proposed method is available online at: https://jiazhang-ml.pub/GRRO-master.zip.

Summary In recent years, multilabel learning has been applied to a lot of application areas and is yet a challenging task. In multilabel learning, an instance often belongs to multiple class labels simultaneously. The labels usually have correlations with others, and mining label correlations is helpful to enhance the multilabel classification performance. Aiming at increasing the accuracy of prediction, Label embedding (LE) is an important technique, and conducive to extracting label information for multilabel learning. In this paper, we present a novel multilabel learning approach via exploiting label correlations, which can be naturally extended to tackle feature selection problem. First, to obtain the discriminative features shared by all labels, the proposed algorithm learns a latent space by employing deep canonical correlation analysis. Then we exploit label correlations by enforcing predictions on similar labels to be similar, thereby improving the prediction performance. Results on several multiple datasets illustrate that the proposed algorithm has the advantages on multilabel classification and feature selection.

Summary The goal of TCM state identification is to identify the patient's syndromes and locations and natures of diseases according to symptoms. Generally, symptoms of a patient are associated with several syndromes and multiple locations and natures of diseases; hence, the TCM state identification is a typical multi‐label problem. In this paper, a new method is proposed to predict syndromes and locations and natures of diseases according to the diagnostic information of TCM. In detail, the correlation between features and the correlation between class labels are combined into a new uniform feature space. After that, the MDMR algorithm is used to select the most discriminatory features from the new uniform feature space, which is helpful to reduce the data dimensionality. Lastly, a KNN‐like algorithm is modified to calculate the label similarity of test data, and the finite set of labels of test data is predicted by ML‐KNN. In this paper, the test data is collected by Fujian University of Traditional Chinese Medicine according to the theory of TCM and medical ethics. The experiments show that the performance of the proposed method is superior to some other popular methods and is helpful in the identification of health state in TCM.