Multi-view clustering aims to partition data collected from diverse sources based on the assumption that all views are complete. However, such prior assumption is hardly satisfied in many real-world applications, resulting in the incomplete multi-view learning problem. The existing attempts on this problem still have the following limitations: 1) the underlying semantic information of the missing views is commonly ignored; 2) The local structure of data is not well explored; 3) The importance of different views is not effectively evaluated. To address these issues, this paper proposes a Unified Embedding Alignment Framework (UEAF) for robust incomplete multi-view clustering. In particular, a locality-preserved reconstruction term is introduced to infer the missing views such that all views can be naturally aligned. A consensus graph is adaptively learned and embedded via the reverse graph regularization to guarantee the common local structure of multiple views and in turn can further align the incomplete views and inferred views. Moreover, an adaptive weighting strategy is designed to capture the importance of different views. Extensive experimental results show that the proposed method can significantly improve the clustering performance in comparison with some state-of-the-art methods.

https://www.aaai.org/ojs/index.php/AAAI/article/download/4478/4356

Unified Embedding Alignment with Missing Views Inferring for Incomplete Multi-View Clustering

In real-world applications, it is often that the collected multi-view data are incomplete, i.e., some views of samples are absent. Existing clustering methods for incomplete multi-view data all focus on obtaining a common representation or graph from the available views but neglect the hidden information of missing views and information imbalance of different views. To solve these problems, a novel method, called adaptive graph completion based incomplete multi-view clustering (AGC_IMC), is proposed in this paper. Specifically, AGC_IMC develops a joint framework for graph completion and consensus representation learning, which mainly contains three components, i.e., within-view preservation, between-view inferring, and consensus representation learning. To reduce the negative influence of information imbalance, AGC_IMC introduces some adaptive weights to balance the importance of different views during the consensus representation learning. Importantly, AGC_IMC has the potential to recover the similarity graphs of all views with the optimal cluster structure, which encourages it to obtain a more discriminative consensus representation. Experimental results on five well-known datasets show that AGC_IMC significantly outperforms the state-of-the-art methods.

Adaptive Graph Completion Based Incomplete Multi-View Clustering

Deep learning in generating radiology reports: A survey.

Graph Learning has emerged as a promising technique for multi-view clustering due to its efficiency of learning a unified graph from multiple views. Previous multi-view graph learning methods mainly try to exploit the multi-view consistency to boost learning performance. However, these methods ignore the prevalent multi-view diversity which may be induced by noise, corruptions, or even view-specific attributes. In this paper, we propose to simultaneously and explicitly leverage the multi-view consistency and the multi-view diversity in a unified framework. The consistent parts are further fused to our target graph with a clear clustering structure, on which the cluster label to each instance can be directly allocated without any postprocessing such as k-means in classical spectral clustering. In addition, our model can automatically assign suitable weight for each view based on its clustering capacity. By leveraging the subtasks of measuring the diversity of graphs, integrating the consistent parts with automatically learned weights, allocating cluster label to each instance in a joint framework, each subtask can be alternately boosted by utilizing the results of the others towards an overall optimal solution. Extensive experimental results on several benchmark multi-view datasets demonstrate the effectiveness of our model in comparison to several state-of-the-art algorithms.

Measuring Diversity in Graph Learning: A Unified Framework for Structured Multi-view Clustering

The development of information gathering and extraction technology has led to the popularity of multi-view data, which enables samples to be seen from numerous perspectives. Multi-view clustering (MVC), which groups data samples by leveraging complementary and consensual information from several views, is gaining popularity. Despite the rapid evolution of MVC approaches, there has yet to be a study that provides a full MVC roadmap for both stimulating technical improvements and orienting research newbies to MVC. In this article, we review recent MVC techniques with the purpose of exhibiting the concepts of popular methodologies and their advancements. This survey not only serves as a unique MVC comprehensive knowledge for researchers but also has the potential to spark new ideas in MVC research. We summarise a large variety of current MVC approaches based on two technical mechanisms: heuristic-based multi-view clustering (HMVC) and neural network-based multi-view clustering (NNMVC). We end with four technological approaches within the category of HMVC: nonnegative matrix factorisation, graph learning, latent representation learning, and tensor learning. Deep representation learning and deep graph learning are two technical methods that we demonstrate in NNMVC. We also show 15 publicly available multi-view datasets and examine how representative MVC approaches perform on them. In addition, this study identifies the potential research directions that may require further investigation in order to enhance the further development of MVC. 

A Comprehensive Survey on Multi-view Clustering

The invention discloses a path planning method in a data collection process of an unmanned plane, and aims at a problem of data loss caused by the limiting of the energy of the unmanned plane. The method gives full consideration to the abnormal data which may be collected at any time and barriers which may appear in an environment at any time, achieves the dynamic planning of a flight path of the unmanned plane, and finally enables the data, collected when the energy of the unmanned plane is limited, to have the maximum value.

Path planning method in data collection process of unmanned plane

Improved image clustering with deep semantic embedding

Applying mobile sink to collect data in wireless sensor networks can avoid the energy hole problem and lengthen the networks' life time. Current related researches have achieved significant improvements on reducing the data collection latency in the scenario where the mobile sink has enough or limited energy. However, it will lead to waste of network resource if not taking the spatial and temporal correlation of data into consideration during the collection because of the high similarity and low quality in the collected data. In this paper, we first propose a method named Dynamic Estimation of Data Value (DEDV) to estimate the value of data stored in the nodes dynamically. Furthermore, we define a heuristic method that drives the mobile sink to collect data from the nodes to maximize the value of the collected data in a power-limited mobile sink scenario. Both simulation and experiments results show that our method can achieve at most over 85% of the theoretical maximum data value determined by the OPT model.

DEDV: A Data Collection Method for Mobile Sink Based on Dynamic Estimation of Data Value in WSN

The identification of plants has great significance in the study of plants, and it has applications in plant classification and medical research. With the development of computer vision, plants identification based on deep learning methods can effectively carry out. At present, most of the existing methods use the traditional features such as the shape and texture features for plant identification, and these methods are applicable to plants with large differences in their shapes. Therefore, we develop a multi-branch convolutional neural network with attention (MCNNA) to extract the effective features, and the first part of MCNNA is an attention block, which is used to reduce the influence of background, and the latter part is multi-branch convolutional neural network, which is used to extract the multi-view features through multi-channel. Experiments have shown that our proposed method has better performances for the classification of plants than the traditional methods. We tested our method on the dataset BJFU100 and obtained final accuracy of 97.89%, and we have the accuracy of 93.35% on our own image dataset.

Plant Identification Based on Multi-branch Convolutional Neural Network with Attention

RETRACTED: Diagnosis labeling with disease-specific characteristics mining.

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