Exploiting different representations, or views, of the same object for better clustering has become very popular these days, which is conventionally called multi-view clustering. Generally, it is essential to measure the importance of each individual view, due to some noises, or inherent capacities in description. Many previous works model the view importance as weight, which is simple but effective empirically. In this paper, instead of following the traditional thoughts, we propose a new weight learning paradigm in context of multi-view clustering in virtue of the idea of re-weighted approach, and we theoretically analyze its working mechanism. Meanwhile, as a carefully achieved example, all of the views are connected by exploring a unified Laplacian rank constrained graph, which will be a representative method to compare with other weight learning approaches in experiments. Furthermore, the proposed weight learning strategy is much suitable for multi-view data, and it can be naturally integrated with many existing clustering learners. According to the numerical experiments, the proposed intrinsic weight learning approach is proved effective and practical to use in multi-view clustering.
Many previous multi-view clustering methods have made attempts to address the view variance problem. In @cite_11 , the authors points out that learning the view importance is very necessary, but they simply resort to the extra prior knowledge in the paper. Some others, such as recent approaches @cite_14 @cite_51 , tackle this problem by modeling the separated noise in each view and learn a shared clean data structure. However, this way is not able to cover the second case of view variance because of the gap of real noises and clustering errors by a weak view. As a matter of fact, measuring the view importance with the weight is direct and useful, and many works prefer this way. For convenience, @cite_19 proposes to roughly compute the weights according to the proportion of the graph volume in each view. Obviously, this strategy is manually intervening and much shallow. Practically, most works would prefer to learn the weights automatically or adaptively. That means they usually optimize the objective and weights simultaneously. In the followings, we particularly introduce several types of how the previous works learn the weights which is closely related our work.
@cite_19 @cite_14 @cite_51 @cite_11
2014143538 201974436 2101324110
Clustering on multiple views is witnessing increasing interests in both real-world application and machine learning community. A typical application is to discover communities of joint interests in social network, such as Facebook and Twitter. The network can be simply modeled as a graph in which the nodes are the people while the links show relationship between the people. There may exist many relationships between a pair of nodes, such as classmates, collaborators, playmates and so on. It is important to consider how to use these graphs together rather than a single graph if we want to understand the network and their participants effectively. Motivated by the fact, we present a clustering algorithm using spectral analysis in which multiple graphs are considered to get the clusters. Our study can also be considered as an instance of multi-views learning. The experimental results on UCI data set and Corel image data demonstrate the promising results that validate our proposed algorithm. Multi-view clustering, which seeks a partition of the data in multiple views that often provide complementary information to each other, has received considerable attention in recent years. In real life clustering problems, the data in each view may have considerable noise. However, existing clustering methods blindly combine the information from multi-view data with possibly considerable noise, which often degrades their performance. In this paper, we propose a novel Markov chain method for Robust Multi-view Spectral Clustering (RMSC). Our method has a flavor of lowrank and sparse decomposition, where we firstly construct a transition probability matrix from each single view, and then use these matrices to recover a shared low-rank transition probability matrix as a crucial input to the standard Markov chain method for clustering. The optimization problem of RMSC has a low-rank constraint on the transition probability matrix, and simultaneously a probabilistic simplex constraint on each of its rows. To solve this challenging optimization problem, we propose an optimization procedure based on the Augmented Lagrangian Multiplier scheme. Experimental results on various real world datasets show that the proposed method has superior performance over several state-of-the-art methods for multi-view clustering. We propose a spectral clustering algorithm for the multi-view setting where we have access to multiple views of the data, each of which can be independently used for clustering. Our spectral clustering algorithm has a flavor of co-training, which is already a widely used idea in semi-supervised learning. We work on the assumption that the true underlying clustering would assign a point to the same cluster irrespective of the view. Hence, we constrain our approach to only search for the clusterings that agree across the views. Our algorithm does not have any hyperparameters to set, which is a major advantage in unsupervised learning. We empirically compare with a number of baseline methods on synthetic and real-world datasets to show the efficacy of the proposed algorithm.