Optimizing exchange confidence during collaborative clustering

Sublime, Jérémie; Maurel, Denis; Grozavu, Nistor; Matei, Basarab; Bennani, Younès

doi:10.1109/ijcnn.2018.8489758

Cited by 5 publications

(6 citation statements)

References 21 publications

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“…This is achieved using Karush Kuhn Tucker (KKT) optimization and the result gives an insight on the importance of diversity to achieve positive collaborations. This first contribution is an extension of two previous conference papers [14,15].…”

Section: Introductionmentioning

confidence: 76%

“…The two results in Equations (15) and 21are a continuity as both encourage to different degrees collaboration with algorithms that have similar partition, and thus a low diversity. When analyzing the expression of the parameter free β * ji from Equation 23, we can see that the interpretation is the same: the more similar the partitions, the stronger the collaboration weights.…”

Section: Results Interpretationmentioning

confidence: 99%

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Study on the Influence of Diversity and Quality in Entropy Based Collaborative Clustering

Sublime

Cabanès

Matei

2019

Entropy

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The aim of collaborative clustering is to enhance the performances of clustering algorithms by enabling them to work together and exchange their information to tackle difficult data sets. The fundamental concept of collaboration is that clustering algorithms operate locally but collaborate by exchanging information about the local structures found by each algorithm. This kind of collaborative learning can be beneficial to a wide number of tasks including multi-view clustering, clustering of distributed data with privacy constraints, multi-expert clustering and multi-scale analysis. Within this context, the main difficulty of collaborative clustering is to determine how to weight the influence of the different clustering methods with the goal of maximizing the final results and minimizing the risk of negative collaborations—where the results are worse after collaboration than before. In this paper, we study how the quality and diversity of the different collaborators, but also the stability of the partitions can influence the final results. We propose both a theoretical analysis based on mathematical optimization, and a second study based on empirical results. Our findings show that on the one hand, in the absence of a clear criterion to optimize, a low diversity pool of solution with a high stability are the best option to ensure good performances. And on the other hand, if there is a known criterion to maximize, it is best to rely on a higher diversity pool of solution with a high quality on the said criterion. While our approach focuses on entropy based collaborative clustering, we believe that most of our results could be extended to other collaborative algorithms.

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Section: Introductionmentioning

confidence: 76%

Section: Results Interpretationmentioning

confidence: 99%

Study on the Influence of Diversity and Quality in Entropy Based Collaborative Clustering

Sublime

Cabanès

Matei

2019

Entropy

Self Cite

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“…Given the use of low-resolution datasets in subspace clustering, employing larger convolutional kernels or deeper network often results in the loss of fine-grained details, while smaller kernels may hinder the extraction of deep-level information. Therefore, we set three-layer encoders with [10,20,30] channels correspondingly and adopt [4 × 4, 3 × 3, 4 × 4] kernel size to extract the representations of each view. We also employ the Mish activation function to ensure smoother gradients during the training process.…”

Section: Multi-view Convolutional Encodersmentioning

confidence: 99%

“…Recently, many multi-view clustering methods have been developed. Existing works about multiview subspace clustering can be divided into different categories, i.e., non-negative matrix factorization (NMF) framework [6][7][8][9], collaborative clustering methods [10][11][12], co-training methods [13][14][15], self-expressive based methods [16,17] and deep-learning-based methods [18,19]. NMF-based methods aim to obtain the partitioning of the data by a low-rank decomposition of the data matrix, and it is proved to be effective where the subspaces of data points are independent of each other.…”

Section: Introductionmentioning

confidence: 99%

Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix

Shi,

Zhao

2023

Applied Sciences

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Deep Multi-view Subspace Clustering is a powerful unsupervised learning technique for clustering multi-view data, which has achieved significant attention during recent decades. However, most current multi-view clustering methods rely on learning self-expressive layers to obtain the ultimate clustering results, where the size of the self-expressive matrix increases quadratically with the number of input data points, making it difficult to handle large-scale datasets. Moreover, since multiple views are rich in information, both consistency and specificity of the input images need to be considered. To solve these problems, we propose a novel deep multi-view clustering approach based on the reconstructed self-expressive matrix (DCRSM). We use a reconstruction module to approximate self-expressive coefficients using only a small number of training samples, while the conventional self-expressive model must train the network with entire datasets. We also use shared layers and specific layers to integrate consistent and specific information of features to fuse information between views. The proposed DCRSM is extensively evaluated on multiple datasets, including Fashion-MNIST, COIL-20, COIL-100, and YTF. The experimental results demonstrate its superiority over several existing multi-view clustering methods, achieving an improvement between 1.94% and 4.2% in accuracy and a maximum improvement of 4.5% in NMI across different datasets. Our DCRSM also yields competitive results even when trained by 50% samples of the whole datasets.

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“…The weighting method is presented in this paper and called Masked Weighting Method. The goal of this method is to (1) combine the information from different views, (2) reduce the weight of views with information which could hinder the cooperative reconstruction process [3], [4], and (3) reduce the impact of missing data during the unsupervised learning process [5].…”

Section: Introductionmentioning

confidence: 99%

Deep Cooperative Reconstruction with Security Constraints in multi-view environments

Maurel

Lefèbvre

Sublime

2020

2020 International Conference on Data Mining Workshops (ICDMW)

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Nowadays, we can observe a multiplication of multiview data in domains such as marketing, bank administration, survey analysis, or social networks: We are dealing with large data bases that share a fair amount of data representing the same individual with different features depending on the data base.In this context, one can use Machine Learning methods to analyze this fragmented data across several heterogeneous sources (called views). Such analysis is subject to several difficulties: First, not all individual will be present and represented in all data sites and views. And second, this type of cross site analysis raises several ethical questions on privacy issues as no local site should have direct access to data from the other sources.To solve these problems, we present a method called the Cooperative Reconstruction System which aims at reconstructing information missing in some views in a multi-view context using information available in the other views. Furthermore, our method considers privacy issues and therefore achieves said reconstruction without direct data transfer from one view to another.

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Optimizing exchange confidence during collaborative clustering

Cited by 5 publications

References 21 publications

Study on the Influence of Diversity and Quality in Entropy Based Collaborative Clustering

Study on the Influence of Diversity and Quality in Entropy Based Collaborative Clustering

Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix

Deep Cooperative Reconstruction with Security Constraints in multi-view environments

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