Adaptive Graph Regularization Discriminant Nonnegative Matrix Factorization for Data Representation

Zhang, Lin; Z, Liu; Wang, Lin; Pu, Jiexin

doi:10.1109/access.2019.2933877

Cited by 9 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An affinity matrix is then obtained by W = |C| + |C| . There are some other affinity matrix construction methods, such as the decision tree based approach [19], fuzzy logic based approach [20], probabilistic geodesic distance based approach [21], nonnegative matrix factorization based approach [22], affinity and penalty constrained approach [23], one-step approach that combines affinity learning and subspace learning [24], [25], auto-encoder based approach [26], etc. The brief workflow of the proposed self-supervised diffusion method.…”

Section: A Affinity Matrix Constructionmentioning

confidence: 99%

Affinity Learning Via Self-Supervised Diffusion for Spectral Clustering

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Spectral clustering makes use of the spectrum of an input affinity matrix to segment data into disjoint clusters. The performance of spectral clustering depends heavily on the quality of the affinity matrix. Commonly used affinity matrices are constructed by either the Gaussian kernel or the self-expressive model with sparse or low-rank constraints. A technique called diffusion which acts as a post-process has recently shown to improve the quality of the affinity matrix significantly, by taking advantage of the contextual information. In this paper, we propose a variant of the diffusion process, named Self-Supervised Diffusion, which incorporates clustering result as feedback to provide supervisory signals for the diffusion process. The proposed method contains two stages, namely affinity learning with diffusion and spectral clustering. It works in an iterative fashion, where in each iteration the clustering result is utilized to calculate a pseudolabel similarity so that it can aid the affinity learning stage in the next iteration. Extensive experiments on both synthetic and real-world data have demonstrated that the proposed method can learn accurate and robust affinity, and thus achieves superior clustering performance.

show abstract

Section: A Affinity Matrix Constructionmentioning

confidence: 99%

Affinity Learning Via Self-Supervised Diffusion for Spectral Clustering

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In the practical application, some labels are included in the original data and the geometrical structure cannot be fully utilized in most NMF variants. To address this issue, a more accurate geometrical structure and a few label information were considered to achieve better image clustering performance [17].…”

Section: Introductionmentioning

confidence: 99%

Binary Codes Based on Non-Negative Matrix Factorization for Clustering and Retrieval

et al. 2020

View full text Add to dashboard Cite

Traditional non-negative matrix factorization methods cannot learn the subspace from the high-dimensional data space composed of binary codes. One hopes to discover a compact parts-based representation composed of binary codes, which can uncover the intrinsic information and simultaneously respect the geometric structure of the original data. For this purpose, we introduce discrete hashing methods and propose a novel non-negative matrix factorization to generate binary codes from the original data. In this paper, we construct an affinity graph to encode the geometrical structure of the original data, and the learned binary code subspace achieved by matrix factorization respects the structure. The proposed problem can be formulated as a mixed integer optimization problem. Therefore, we transform it into several sub-problems including an integer optimization problem, two convex problems with the non-negative constraint and a quadratic programming problem. Optimizing each sub-problem alternately until we achieve a local optimal solution. Image clustering and retrieval on image datasets show the excellent performance of our method in comparison to other dimensional reduction methods.

show abstract

Real-time monitoring of sports performance based on ensemble learning algorithm and neural network

Zhou

Zhang

2023

Soft Comput

View full text Add to dashboard Cite

In order to improve the performance of sports performance prediction, based on computational learning algorithms, this article builds a sports performance prediction model based on ensemble learning algorithms under the guidance of machine learning ideas. Moreover, this article applies the cascade principle to improve the accuracy of the model and determines the cascade structure, studies the characteristics of spatio-temporal sequence data and the modeling methods of spatio-temporal sequence models, and combines the idea of selective integration learning to improve the spatio-temporal neural network model. In addition, this paper uses the L1 regularization method to sparsely weight and combine multiple STELM models to achieve selective integration. Finally, this paper designs experiments to predict the performance of this model in sports performance prediction. The research results show that the prediction results of the sports performance prediction model constructed in this paper are accurate.

show abstract

Adaptive Graph Regularization Discriminant Nonnegative Matrix Factorization for Data Representation

Cited by 9 publications

References 32 publications

Affinity Learning Via Self-Supervised Diffusion for Spectral Clustering

Affinity Learning Via Self-Supervised Diffusion for Spectral Clustering

Binary Codes Based on Non-Negative Matrix Factorization for Clustering and Retrieval

Real-time monitoring of sports performance based on ensemble learning algorithm and neural network

Contact Info

Product

Resources

About