A Semi-NMF-PCA Unified Framework for Data Clustering

Allab, Kais; Labiod, Lazhar; Nadif, Mohamed

doi:10.1109/tkde.2016.2606098

Cited by 83 publications

(30 citation statements)

References 23 publications

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“…Image related: Some work embeds graphs constructed from images, and then use the embedding for image classification ( [81], [82]), image clustering [101], image segmentation [154], pattern recognition [80], and so on.…”

Section: Other Applicationsmentioning

confidence: 99%

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

Cai

Zheng

Chang

2018

IEEE Trans. Knowl. Data Eng.

1,722

885

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Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximumly preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work address these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques and application scenarios.

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Section: Other Applicationsmentioning

confidence: 99%

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

Cai

Zheng

Chang

2018

IEEE Trans. Knowl. Data Eng.

1,722

885

View full text Add to dashboard Cite

show abstract

“…Furthermore, we have shown that it was possible to improve the consensus quality through the use of finite mixture models, allowing more powerful underlying settings than cluster-based consensus involving plain similarities or distances. A future work will be to investigate the use of cluster ensembles for other recent clustering algorithms [1][2][3]19,20].…”

Section: Resultsmentioning

confidence: 99%

A Consensus Approach to Improve NMF Document Clustering

Febrissy

Nadif

2020

Lecture Notes in Computer Science

Self Cite

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Nonnegative Matrix Factorization (NMF) which was originally designed for dimensionality reduction has received throughout the years a tremendous amount of attention for clustering purposes in several fields such as image processing or text mining. However, despite its mathematical elegance and simplicity, NMF has exposed a main issue which is its strong sensitivity to starting points, resulting in NMF struggling to converge toward an optimal solution. On another hand, we came to explore and discovered that even after providing a meaningful initialization, selecting the solution with the best local minimum was not always leading to the one having the best clustering quality, but somehow a better clustering could be obtained with a solution slightly off in terms of criterion. Therefore in this paper, we undertake to study the clustering characteristics and quality of a set of NMF best solutions and provide a method delivering a better partition using a consensus made of the best NMF solutions.

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“…Since the original data matrix in the real world usually contains very complex information, the NMF method based on a one-layer structure is difficult to mine the high-level features of the data [29]. Inspired by recent advances in deep learning, Trigeorgis et al [22] proposed a Deep semi-Nonnegative Matrix Factorization (Deep semi-NMF) algorithm, which can construct a deep network by factorizing the data many times through the semi-NMF method [31], so that the relationships between the different layers can be exploited to reveal the intrinsic high-level features of the original data. The main idea of Deep semi-NMF is to decompose the coding coefficient matrix V several times through the semi-NMF algorithm, and take the decomposed results as the input of the next layer, and finally build a deep network structure.…”

Section: Deep Semi-nonnegative Matrix Factorizationmentioning

confidence: 99%

Sparse Dual Graph-Regularized Deep Nonnegative Matrix Factorization for Image Clustering

Guo

2021

IEEE Access

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Deep nonnegative matrix factorization (Deep NMF) as an emerging technique for image clustering has attracted more and more attention. This is because it can effectively reduce high-dimensional data and reveal the latent hierarchical information of the complex data. However, two limitations may still deteriorate their performances: (1) the local invariance of the input data is insufficiently explored, that is, the intrinsic geometrical structures of the original data in the data and feature spaces are not considered simultaneously; (2) the sparseness that can greatly improve the ability of learning parts is also ignored. In this paper, we propose a novel approach to address the above two problems, referred to as Sparse Dual Graph-regularized Deep Nonnegative Matrix Factorization (SDG Deep NMF), which can learn sparse and informative deep features while sufficiently exploring the local invariance of the data to discover valuable information underlying the input data. Specifically, SDG Deep NMF learns the informative deep features by performing the dual graph regularization in the deep NMF framework, which can respect the intrinsic geometrical structures of the input data in the data and feature spaces while mining the data information in hidden layers. Meanwhile, SDG Deep NMF also imposes sparse constraints on the basis matrix during the feature learning to improve the part-based learning capabilities. Moreover, we construct the objective function of SDG Deep NMF in the form of the Euclidean distance for convenience, the iterative updating scheme is chosen to optimize it. Comprehensive experiments on four benchmark datasets can demonstrate the effectiveness of the proposed approach in image clustering. INDEX TERMSDeep nonnegative matrix factorization, dual graph regularization, sparse constraints, image clustering.

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A Semi-NMF-PCA Unified Framework for Data Clustering

Abstract: International audienc

Cited by 83 publications

References 23 publications

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

A Consensus Approach to Improve NMF Document Clustering

Sparse Dual Graph-Regularized Deep Nonnegative Matrix Factorization for Image Clustering

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