This paper focuses on unpaired multi-view clustering (UMC), a challenging
problem where paired observed samples are unavailable across multiple views.
The goal is to perform effective joint clustering using the unpaired observed
samples in all views. In incomplete multi-view clustering, existing methods
typically rely on sample pairing between views to capture their complementary.
However, that is not applicable in the case of UMC. Hence, we aim to extract
the consistent cluster structure across views. In UMC, two challenging issues
arise: uncertain cluster structure due to lack of label and uncertain pairing
relationship due to absence of paired samples. We assume that the view with a
good cluster structure is the reliable view, which acts as a supervisor to
guide the clustering of the other views. With the guidance of reliable views, a
more certain cluster structure of these views is obtained while achieving
alignment between reliable views and other views. Then we propose Reliable view
Guidance with one reliable view (RG-UMC) and multiple reliable views (RGs-UMC)
for UMC. Specifically, we design alignment modules with one reliable view and
multiple reliable views, respectively, to adaptively guide the optimization
process. Also, we utilize the compactness module to enhance the relationship of
samples within the same cluster. Meanwhile, an orthogonal constraint is applied
to latent representation to obtain discriminate features. Extensive experiments
show that both RG-UMC and RGs-UMC outperform the best state-of-the-art method
by an average of 24.14\% and 29.42\% in NMI, respectively.

该研究专注于非配对多视图聚类问题，提出了一种可靠视图引导的方法来解决跨视图聚类中的不确定聚类结构和样本配对关系不确定的问题，并通过对齐模块和紧凑性模块进一步优化聚类效果，实验证明该方法的优越性。

无配对多视角聚类中的可靠视角指导

Unpaired Multi-view Clustering via Reliable View Guidance

Clustering with incomplete views is a challenge in multi-view clustering. In
this paper, we provide a novel and simple method to address this issue.
Specifically, the proposed method simultaneously exploits the local information
of each view and the complementary information among views to learn the common
latent representation for all samples, which can greatly improve the
compactness and discriminability of the obtained representation. Compared with
the conventional graph embedding methods, the proposed method does not
introduce any extra regularization term and corresponding penalty parameter to
preserve the local structure of data, and thus does not increase the burden of
extra parameter selection. By imposing the orthogonal constraint on the basis
matrix of each view, the proposed method is able to handle the out-of-sample.
Moreover, the proposed method can be viewed as a unified framework for
multi-view learning since it can handle both incomplete and complete multi-view
clustering and classification tasks. Extensive experiments conducted on several
multi-view datasets prove that the proposed method can significantly improve
the clustering performance.

提出了一种使用本地信息和视图之间的互补信息来学习所有样本的通用潜在表示的简单方法，通过对每个视图的基础矩阵施加正交约束，该方法能够处理缺失视图的多视图聚类和分类任务，并证明在多个多视图数据集上能显著提高聚类性能。

基于图正则化矩阵分解的不完整多视图聚类

Incomplete Multi-view Clustering via Graph Regularized Matrix  Factorization

The problem of learning long-term dependencies in sequences using Recurrent
Neural Networks (RNNs) is still a major challenge. Recent methods have been
suggested to solve this problem by constraining the transition matrix to be
unitary during training which ensures that its norm is equal to one and
prevents exploding gradients. These methods either have limited expressiveness
or scale poorly with the size of the network when compared with the simple RNN
case, especially when using stochastic gradient descent with a small mini-batch
size. Our contributions are as follows; we first show that constraining the
transition matrix to be unitary is a special case of an orthogonal constraint.
Then we present a new parametrisation of the transition matrix which allows
efficient training of an RNN while ensuring that the matrix is always
orthogonal. Our results show that the orthogonal constraint on the transition
matrix applied through our parametrisation gives similar benefits to the
unitary constraint, without the time complexity limitations.

本文介绍了通过约束转移矩阵为正交矩阵来解决序列学习中的长期依赖性问题的方法，并针对简单 RNN 的情况提出了一种新的参数化方案，将正交约束应用于转移矩阵，以提高训练效率。