Journal ArticleDOI
Generalized Incomplete Multiview Clustering With Flexible Locality Structure Diffusion
TLDR
A novel graph-regularized matrix factorization model is developed to preserve the local geometric similarities of the learned common representations from different views and the semantic consistency constraint is introduced to stimulate these view-specific representations toward a unified discriminative representation.Abstract:
An important underlying assumption that guides the success of the existing multiview learning algorithms is the full observation of the multiview data. However, such rigorous precondition clearly violates the common-sense knowledge in practical applications, where in most cases, only incomplete fractions of the multiview data are given. The presence of the incomplete settings generally disables the conventional multiview clustering methods. In this article, we propose a simple but effective incomplete multiview clustering (IMC) framework, which simultaneously considers the local geometric information and the unbalanced discriminating powers of these incomplete multiview observations. Specifically, a novel graph-regularized matrix factorization model, on the one hand, is developed to preserve the local geometric similarities of the learned common representations from different views. On the other hand, the semantic consistency constraint is introduced to stimulate these view-specific representations toward a unified discriminative representation. Moreover, the importance of different views is adaptively determined to reduce the negative influence of the unbalanced incomplete views. Furthermore, an efficient learning algorithm is proposed to solve the resulting optimization problem. Extensive experimental results performed on several incomplete multiview datasets demonstrate that the proposed method can achieve superior clustering performance in comparison with some state-of-the-art multiview learning methods.read more
Citations
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Journal ArticleDOI
Deep learning on image denoising: An overview.
TL;DR: A comparative study of deep techniques in image denoising by classifying the deep convolutional neural networks for additive white noisy images, the deep CNNs for real noisy images; the deepCNNs for blind Denoising and the deep network for hybrid noisy images.
Journal ArticleDOI
Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network
Yudong Zhang,Yudong Zhang,Suresh Chandra Satapathy,David S. Guttery,Juan Manuel Górriz,Shuihua Wang,Shuihua Wang +6 more
TL;DR: The BDR-CNN-GCN showed improved performance compared to five proposed neural network models and 15 state-of-the-art breast cancer detection approaches, proving to be an effective method for data augmentation and improved detection of malignant breast masses.
Journal ArticleDOI
Adaptive Graph Completion Based Incomplete Multi-View Clustering
TL;DR: Experimental results on five well-known datasets show that AGC_IMC significantly outperforms the state-of-the-art methods and 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.
Journal ArticleDOI
CMC: A consensus multi-view clustering model for predicting Alzheimer's disease progression.
TL;DR: A new multi-view clustering model called Consensus Multi-view Clustering (CMC) based on nonnegative matrix factorization for predicting the multiple stages of AD progression is proposed and can improve the predication performance of AD, but also can screen and classify the symptoms of different AD's phases.
Journal ArticleDOI
CGNet: A graph-knowledge embedded convolutional neural network for detection of pneumonia
TL;DR: A deep learning framework for a binary classification task that classifies chest X-ray images into normal and pneumonia based on the proposed CGNet, which achieved the best accuracy, sensitivity, and specificity at 0.9795 on a public pneumonia dataset.
References
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Avrim Blum,Tom M. Mitchell +1 more
TL;DR: A PAC-style analysis is provided for a problem setting motivated by the task of learning to classify web pages, in which the description of each example can be partitioned into two distinct views, to allow inexpensive unlabeled data to augment, a much smaller set of labeled examples.
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Proceedings Article
Multimodal Deep Learning
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Proceedings Article
Co-regularized Multi-view Spectral Clustering
TL;DR: A spectral clustering framework is proposed that achieves this goal by co-regularizing the clustering hypotheses, and two co- regularization schemes are proposed to accomplish this.
Journal ArticleDOI
An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling
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