Deep semi-nonnegative matrix factorization with elastic preserving for data representation

Shu, Zhenqiu; Wu, Xiaojun; Hu, Cong; You, Congzhe; Fan, Huiqing

doi:10.1007/s11042-020-09766-w

Cited by 16 publications

(9 citation statements)

References 21 publications

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“…Thus, each image is represented by a 1024-dimensional vector. Similarly, the COIL100 [24] image database has the same settings, which contains 7200 images of 100 objects.…”

Section: A Datasets Descriptionmentioning

confidence: 99%

“…Moreover, Qu et al [23] introduced a Graph Regularized Deep semi-Nonnegative Matrix Factorization (GR Deep semi-NMF) algorithm, which preserves the geometric structure information of the data while learning high-level feature representations. Meanwhile, Shu et al [24] proposed a Deep Semi-Nonnegative Matrix Factorization with Elastic Preserving (Deep Semi-NMF-EP) algorithm for data representation, which can effectively preserve the elasticity of data and learn a better representation of high-dimensional data. After that, Yu et al [25] presented a Deep Non-Smooth Nonnegative Matrix Factorization (Deep nsNMF) algorithm, which not only gives the parts-based features due to the nonnegativity constraints but also creates higher-level, more abstract features by combing lower-level ones.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Thus, each image is represented by a 1024-dimensional vector. Similarly, the COIL100 [24] image database has the same settings, which contains 7200 images of 100 objects.…”

Section: A Datasets Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Guo

2021

IEEE Access

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“…In recent years, deep learning has exhibited outstanding performance in feature representation tasks [18][19][20]. Therefore, many researchers have introduced deep learning into matrix factorization and proposed a large number of deep feature representation methods [21][22][23][24][25][26][27]. Ahn et al [21] proposed multilayer nonnegative matrix factorization (MNMF).…”

Section: Introductionmentioning

confidence: 99%

Adaptive‐Weighted Multiview Deep Basis Matrix Factorization for Multimedia Data Analysis

Liu

Dai

et al. 2021

Wireless Communications and Mobile Computing

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Feature representation learning is a key issue in artificial intelligence research. Multiview multimedia data can provide rich information, which makes feature representation become one of the current research hotspots in data analysis. Recently, a large number of multiview data feature representation methods have been proposed, among which matrix factorization shows the excellent performance. Therefore, we propose an adaptive-weighted multiview deep basis matrix factorization (AMDBMF) method that integrates matrix factorization, deep learning, and view fusion together. Specifically, we first perform deep basis matrix factorization on data of each view. Then, all views are integrated to complete the procedure of multiview feature learning. Finally, we propose an adaptive weighting strategy to fuse the low-dimensional features of each view so that a unified feature representation can be obtained for multiview multimedia data. We also design an iterative update algorithm to optimize the objective function and justify the convergence of the optimization algorithm through numerical experiments. We conducted clustering experiments on five multiview multimedia datasets and compare the proposed method with several excellent current methods. The experimental results demonstrate that the clustering performance of the proposed method is better than those of the other comparison methods.

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“…In recent years, researchers found that the relationships between data in real applications usually show high dimension nonlinear, so the aforementioned linear representation approaches can hardly achieve good performance. Many researchers paid more attention on revealing the nonlinear relationship between data points of interests [16][17][18][19][20][21][22][23][24][25][26][27]. For example, Wang et al [28] explored the criterion of Locally Linear Embedding (LLE) and used it to construct the graph by computing the weights between the pairs of samples.…”

Section: Introductionmentioning

confidence: 99%

Robust Graph Structure Learning for Multimedia Data Analysis

Zhou

Gong

Guo

et al. 2021

Wireless Communications and Mobile Computing

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With the rapid development of computer network technology, we can acquire a large amount of multimedia data, and it becomes a very important task to analyze these data. Since graph construction or graph learning is a powerful tool for multimedia data analysis, many graph-based subspace learning and clustering approaches have been proposed. Among the existing graph learning algorithms, the sample reconstruction-based approaches have gone the mainstream. Nevertheless, these approaches not only ignore the local and global structure information but also are sensitive to noise. To address these limitations, this paper proposes a graph learning framework, termed Robust Graph Structure Learning (RGSL). Different from the existing graph learning approaches, our approach adopts the self-expressiveness of samples to capture the global structure, meanwhile utilizing data locality to depict the local structure. Specially, in order to improve the robustness of our approach against noise, we introduce l 2 , 1 -norm regularization criterion and nonnegative constraint into the graph construction process. Furthermore, an iterative updating optimization algorithm is designed to solve the objective function. A large number of subspace learning and clustering experiments are carried out to verify the effectiveness of the proposed approach.

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Deep semi-nonnegative matrix factorization with elastic preserving for data representation

Cited by 16 publications

References 21 publications

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

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

Adaptive‐Weighted Multiview Deep Basis Matrix Factorization for Multimedia Data Analysis

Robust Graph Structure Learning for Multimedia Data Analysis

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