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.
Another approach to smoothen the weight distribution is to introduce a parameter as the exponent of each @math where ( _3 > 1 ). Due to the free of the regularization term, numerous multi-view clustering works @cite_8 @cite_42 @cite_9 @cite_2 @cite_31 have adopted it. Fixing @math , the solution of the subproblem can be given as
@cite_8 @cite_9 @cite_42 @cite_2 @cite_31
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Different features describe different views of visual appearance, multi-view based methods can integrate the information contained in each view and improve the image clustering performance. Most of the existing methods assume that the importance of one type of feature is the same to all the data. However, the visual appearance of images are different, so the description abilities of different features vary with different images. To solve this problem, we propose a group-aware multi-view fusion approach. Images are partitioned into groups which consist of several images sharing similar visual appearance. We assign different weights to evaluate the pairwise similarity between different groups. Then the clustering results and the fusion weights are learned by an iterative optimization procedure. Experimental results indicate that our approach achieves promising clustering performance compared with the existing methods. Exploiting multiple representations, or views, for the same set of instances within a clustering framework is a popular practice for boosting clustering accuracy. However, some of the available sources may be misleading (due to noise, errors in measurement etc.) in revealing the true structure of the data, thus, their inclusion in the clustering process may have negative influence. This aspect seems to be overlooked in the multi-view literature where all representations are equally considered. In this work, views are expressed in terms of given kernel matrices and a weighted combination of the kernels is learned in parallel to the partitioning. Weights assigned to kernels are indicative of the quality of the corresponding views' information. Additionally, the combination scheme incorporates a parameter that controls the admissible sparsity of the weights to avoid extremes and tailor them to the data. Two efficient iterative algorithms are proposed that alternate between updating the view weights and recomputing the clusters to optimize the intra-cluster variance from different perspectives. The conducted experiments reveal the effectiveness of our methodology compared to other multi-view methods. Multi-view clustering has become a popular clustering technique in recent years due to its ability to analyze data collected from multiple sources or represented by multiple views. In this paper, we propose a novel multi-view clustering approach termed weighted multi-view online competitive clustering (WMLCC). We simultaneously exploit the variable weighting strategy and the online competitive learning in our approach and cast the multi-view clustering problem into an optimization problem. The multi-view clustering result can be obtained by optimizing a new objective function. We conduct Multi-view clustering has become a popular clustering technique in recent years due to its ability to analyze data collected from multiple sources or represented by multiple views. In this paper, we propose a novel multi-view clustering approach termed weighted multi-view online competitive clustering (WMLCC). We simultaneously exploit the variable weighting strategy and the online competitive learning in our approach and cast the multi-view clustering problem into an optimization problem. The multi-view clustering result can be obtained by optimizing a new objective function. We conduct experiments on two real-world multi-view datasets. Experimental results demonstrate the effectiveness and efficiency of our approach. Multiview clustering partitions a dataset into groups by simultaneously considering multiple representations (views) for the same instances. Hence, the information available in all views is exploited and this may substantially improve the clustering result obtained by using a single representation. Usually, in multiview algorithms all views are considered equally important, something that may lead to bad cluster assignments if a view is of poor quality. To deal with this problem, we propose a method that is built upon exemplar-based mixture models, called convex mixture models (CMMs). More specifically, we present a multiview clustering algorithm, based on training a weighted multiview CMM, that associates a weight with each view and learns these weights automatically. Our approach is computationally efficient and easy to implement, involving simple iterative computations. Experiments with several datasets confirm the advantages of assigning weights to the views and the superiority of our framework over single-view and unweighted multiview CMMs, as well as over another multiview algorithm which is based on kernel canonical correlation analysis.