Adaptive Sample-level Graph Combination for Partial Multiview Clustering

High-Order Correlation Preserved Incomplete Multi-View Subspace Clustering

Incomplete multi-view clustering aims to exploit the information of multiple incomplete views to partition data into their clusters. Existing methods only utilize the pair-wise sample correlation and pair-wise view correlation to improve the clustering performance but neglect the high-order correlation of samples and that of views. To address this issue, we propose a high-order correlation preserved incomplete multi-view subspace clustering (HCP-IMSC) method which effectively recovers the missing views of samples and the subspace structure of incomplete multi-view data. Specifically, multiple affinity matrices constructed from the incomplete multi-view data are treated as a third-order low rank tensor with a tensor factorization regularization which preserves the high-order view correlation and sample correlation. Then, a unified affinity matrix can be obtained by fusing the view-specific affinity matrices in a self-weighted manner. A hypergraph is further constructed from the unified affinity matrix to preserve the high-order geometrical structure of the data with incomplete views. Then, the samples with missing views are restricted to be reconstructed by their neighbor samples under the hypergraph-induced hyper-Laplacian regularization. Furthermore, the learning of view-specific affinity matrices as well as the unified one, tensor factorization, and hyper-Laplacian regularization are integrated into a unified optimization framework. An iterative algorithm is designed to solve the resultant model. Experimental results on various benchmark datasets indicate the superiority of the proposed method. The code is implemented by using MATLAB R2018a and MindSpore library: https://github.com/ChangTang/HCP-IMSC.

Fs-DSM: Few-Shot Diagram-Sentence Matching via Cross-Modal Attention Graph Model

Diagram-sentence matching is a valuable academic research because it can help learners effectively understand the diagrams with the assisted by sentences. However, there are many uncommon objects, i.e. few-shot contents in diagrams and sentences. The existing methods for image-sentence matching have great limitations when applied to diagrams. Because they focus on the high-frequency objects during training and ignore the uncommon objects. In addition, the specialty leads to the semantic non-intuition of the diagram itself. In this work, we propose a cross-modal attention graph model for the few-shot diagram-sentence matching task named Fs-DSM. Specifically, it is composed of three modules. The graph initialization module regards the region-level diagram features and word-level sentence features as the nodes of Fs-DSM, and edges are represented as similarity between nodes. The information propagation module is a key point of Fs-DSM, in which the few-shot contents are recognized by an uncommon object recognition strategy, and then the nodes are updated by a neighborhood aggregation procedure with cross-modal propagation between all visual and textual nodes, while the edges are recomputed based on the new node features. The global association module integrates the features of regions and words to represent the global diagrams and sentences. By conducting comprehensive experiments in terms of few-shot and conventional image-sentence matching, we demonstrate that Fs-DSM achieves superior performances over the competitors on the AI2D [Formula: see text] diagram dataset and two public benchmark datasets with nature images.

Joint Learning of Latent Similarity and Local Embedding for Multi-View Clustering

Spectral clustering has been an attractive topic in the field of computer vision due to the extensive growth of applications, such as image segmentation, clustering and representation. In this problem, the construction of the similarity matrix is a vital element affecting clustering performance. In this paper, we propose a multi-view joint learning (MVJL) framework to achieve both a reliable similarity matrix and a latent low-dimensional embedding. Specifically, the similarity matrix to be learned is represented as a convex hull of similarity matrices from different views, where the nuclear norm is imposed to capture the principal information of multiple views and improve robustness against noise/outliers. Moreover, an effective low-dimensional representation is obtained by applying local embedding on the similarity matrix, which preserves the local intrinsic structure of data through dimensionality reduction. With these techniques, we formulate the MVJL as a joint optimization problem and derive its mathematical solution with the alternating direction method of multipliers strategy and the proximal gradient descent method. The solution, which consists of a similarity matrix and a low-dimensional representation, is ultimately integrated with spectral clustering or K-means for multi-view clustering. Extensive experimental results on real-world datasets demonstrate that MVJL achieves superior clustering performance over other state-of-the-art methods.