Self-Supervised Deep Correlational Multi-View Clustering

Multi-view Low-rank Preserving Embedding: A novel method for multi-view representation

Semantically consistent multi-view representation learning

In this paper, we propose a deep learning model based on deep multi-view representation learning, to address the video object segmentation task. The proposed model emphasizes the importance of the inherent correlation between video frames and incorporates a multi-view representation learning based on deep canonically correlated autoencoders. The multi-view representation learning in our model provides an efficient mechanism for capturing inherent correlations by jointly extracting useful features and learning better representation into a joint feature space, i.e., shared representation. To increase the training data and the learning capacity, we train the proposed model with pairs of video frames, i.e., Fa and Fb. During the segmentation phase, the deep canonically correlated autoencoders model encodes useful features by processing multiple reference frames together, which is used to detect the frequently reappearing. Our model enhances the state-of-the-art deep learning-based methods that mainly focus on learning discriminative foreground representations over appearance and motion. Experimental results over two large benchmarks demonstrate the ability of the proposed method to outperform competitive approaches and to reach good performances, in terms of semantic segmentation.

Deep multi-view representation learning focuses on training a unified low-dimensional representation for data with multiple sources or modalities. With the rapidly growing attention of graph neural networks, more and more researchers have introduced various graph models into multi-view learning. Although considerable achievements have been made, most existing methods usually propagate information in a single view and fuse multiple information only from the perspective of attributes or relationships. To solve the aforementioned problems, we propose an efficient model termed Dual Fusion-Propagation Graph Neural Network (DFP-GNN) and apply it to deep multi-view clustering tasks. The proposed method is designed with three submodules and has the following merits: a) The proposed view-specific and cross-view propagation modules can capture the consistency and complementarity information among multiple views; b) The designed fusion module performs multi-view information fusion with the attributes of nodes and the relationships among them simultaneously. Experiments on popular databases show that DFP-GNN achieves significant results compared with several state-of-the-art algorithms.

Multiview data, characterized by rich features, are crucial in many machine learning applications. However, effectively extracting intraview features and integrating interview information present significant challenges in multiview learning (MVL). Traditional deep network-based approaches often involve learning multiple layers to derive latent. In these methods, the features of different classes are typically implicitly embedded rather than systematically organized. This lack of structure makes it challenging to explicitly map classes to independent principal subspaces in the feature space, potentially causing class overlap and confusion. Consequently, the capability of these representations to accurately capture the intrinsic structure of the data remains uncertain. In this article, we introduce an innovative multiview representation learning (MVRL) by maximizing two information-theoretic metrics: intraview coding rate reduction and interview mutual information. Specifically, in the intraview representation learning, we aim to optimize feature representations by maximizing the coding rate difference between the entire dataset and individual classes. This process expands the feature representation space while compressing the representations within each class, resulting in more compact feature representations within each viewpoint. Subsequently, we align and fuse these view-specific features through space transformation and cross-sample fusion to achieve consistent representation across multiple views. Finally, we maximize information transmission to maintain consistency and correlation among data representations across views. By maximizing mutual information between the consensus representations and view-specific representations, our method ensures that the learned representations capture more concise intrinsic features and correlations among different views, thereby enhancing the performance and generalization ability of MVL. Experiments show that the proposed methods have achieved excellent performance.

Network representation learning is a key research field in network data mining. In this paper, we propose a novel multi-view network representation algorithm (MVNR), which embeds multi-scale relations of network vertices into the low dimensional representation space. In contrast to existing approaches, MVNR explicitly encodes higher order information using k-step networks. In addition, we introduce the matrix forest index as a kind of network feature, which can be applied to balance the representation weights of different network views. We also research the relevance amongst MVNR and several excellent research achievements, including DeepWalk, node2vec and GraRep and so forth. We conduct our experiment on several real-world citation datasets and demonstrate that MVNR outperforms some new approaches using neural matrix factorization. Specifically, we demonstrate the efficiency of MVNR on network classification, visualization and link prediction tasks.

Multi-view representation learning with Kolmogorov-Smirnov to predict default based on imbalanced and complex dataset

Although multi-view learning has made significant progress over the past few decades, it is still challenging due to the difficulty in modeling complex correlations among different views, especially under the context of view missing. To address the challenge, we propose a novel framework termed Cross Partial Multi-View Networks (CPM-Nets), which aims to fully and flexibly take advantage of multiple partial views. We first provide a formal definition of completeness and versatility for multi-view representation and then theoretically prove the versatility of the learned latent representations. For completeness, the task of learning latent multi-view representation is specifically translated to a degradation process by mimicking data transmission, such that the optimal tradeoff between consistency and complementarity across different views can be implicitly achieved. Equipped with adversarial strategy, our model stably imputes missing views, encoding information from all views for each sample to be encoded into latent representation to further enhance the completeness. Furthermore, a nonparametric classification loss is introduced to produce structured representations and prevent overfitting, which endows the algorithm with promising generalization under view-missing cases. Extensive experimental results validate the effectiveness of our algorithm over existing state of the arts for classification, representation learning and data imputation.

Ensuring safety and security in urban air mobility is of utmost significance. As air traffic becomes more concentrated in urban regions, instances of flight conflicts are on the rise. The complex urban morphology, micro-environmental factors and various flight risks significantly impact flight safety. This paper introduces a comprehensive framework to address these challenges. The framework incorporates random 3D city layouts, encompassing city buildings and terrains to establish a corridor graph structure. Leveraging this graph, a multi-view representation learning approach is proposed, which employs graph neural networks, recurrent neural networks (RNN) and contrastive learning to effectively manage tactical conflicts. Through rigorous testing across diverse scenarios, including the incorporation of uncertainties such as wind turbulence, the model’s performance is extensively evaluated. The conclusive results underscore the robustness and efficacy of the proposed approach in ensuring safety and resolving conflicts within the dynamic urban air mobility landscape.

The low-rank tensor could characterize inner structure and explore high-order correlation among multi-view representations, which has been widely used in multi-view clustering. Existing approaches adopt the tensor nuclear norm (TNN) as a convex approximation of non-convex tensor rank function. However, TNN treats the different singular values equally and over-penalizes the main rank components, leading to sub-optimal tensor representation. In this paper, we devise a better surrogate of tensor rank, namely the tensor logarithmic Schatten- p norm ([Formula: see text]N), which fully considers the physical difference between singular values by the non-convex and non-linear penalty function. Further, a tensor logarithmic Schatten- p norm minimization ([Formula: see text]NM)-based multi-view subspace clustering ([Formula: see text]NM-MSC) model is proposed. Specially, the proposed [Formula: see text]NM can not only protect the larger singular values encoded with useful structural information, but also remove the smaller ones encoded with redundant information. Thus, the learned tensor representation with compact low-rank structure will well explore the complementary information and accurately characterize the high-order correlation among multi-views. The alternating direction method of multipliers (ADMM) is used to solve the non-convex multi-block [Formula: see text]NM-MSC model where the challenging [Formula: see text]NM problem is carefully handled. Importantly, the algorithm convergence analysis is mathematically established by showing that the sequence generated by the algorithm is of Cauchy and converges to a Karush-Kuhn-Tucker (KKT) point. Experimental results on nine benchmark databases reveal the superiority of the [Formula: see text]NM-MSC model.

Multi-view Representation Learning with Decoupled private and shared Propagation

Multi-view Representation Learning with Deep Features for Offline Signature Verification

Multi-view representation learning is challenging because different views contain both the common structure and the complex view specific information. The traditional generative models may not be effective in such situation, since view-specific and common information cannot be well separated, which may cause problems for downstream vision tasks. In this paper, we introduce a multi-view generator model to solve the problem of multi-view generation and recognition in a unified framework. We propose a multi-view alternating back-propagation algorithm to learn multi-view generator networks by allowing them to share common latent factors. Our experiments show that the proposed method is effective for both image generation and recognition. Specifically, we first qualitatively demonstrate that our model can rotate and complete faces accurately. Then we show that our model can achieve state-of-art or competitive recognition performances through quantitative comparisons.

Adversarial correlated autoencoder for unsupervised multi-view representation learning

A multi-view representation learning framework for commonsense knowledge bases