Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Cong-zhe You"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-13 (2023)
Abstract The random matrix (RM) model is a typical extended object-modeling method that has been widely used in extended object tracking. However, existing RM-based filters usually assume that the measurements follow a Gaussian distribution, which ma
Externí odkaz:
https://doaj.org/article/b1ee2bb28927465395fd2c15c649a69d
Publikováno v:
IEEE Access, Vol 8, Pp 133777-133786 (2020)
Nonnegative matrix factorization-based image representation algorithms have been widely applied to deal with high-dimensional data in the past few years. In this paper, we propose a graph regularized constrained nonnegative matrix factorization with
Externí odkaz:
https://doaj.org/article/13c69f3bf63546daaa6f4ad85eec97b2
Publikováno v:
Journal of Algorithms & Computational Technology, Vol 15 (2021)
Recently, in the area of artificial intelligence and machine learning, subspace clustering of multi-view data is a research hotspot. The goal is to divide data samples from different sources into different groups. We proposed a new subspace clusterin
Externí odkaz:
https://doaj.org/article/2ff15642679148a686e1db5be2462fc5
Publikováno v:
Journal of Algorithms & Computational Technology, Vol 15 (2021)
Low-rank representation (LRR) has attracted wide attention of researchers in recent years due to its excellent performance in the exploration of high-dimensional subspace structures. However, in the existing semi-supervised learning problem based on
Externí odkaz:
https://doaj.org/article/857b969273ac4ba6ae49a76cbe016f3f
Publikováno v:
Journal of Algorithms & Computational Technology, Vol 15 (2021)
Externí odkaz:
https://doaj.org/article/6772023db46840eeaaf957df02e20e95
Publikováno v:
Journal of Algorithms & Computational Technology, Vol 15 (2021)
Low-Rank Representation (LRR) and Sparse Subspace Clustering (SSC) are considered as the hot topics of subspace clustering algorithms. SSC induces the sparsity through minimizing the l 1 -norm of the data matrix while LRR promotes a low-rank structur
Externí odkaz:
https://doaj.org/article/d1e3e509a818429e9e339cd4f8850f5f
Autor:
Cong-Zhe You, Xiao-Jun Wu
Publikováno v:
Journal of Algorithms & Computational Technology, Vol 11 (2017)
This paper deals with clustering for multiview data. Multiview clustering has been a research hot spot in many domains or applications, such as information retrieval, biology, chemistry, and marketing. Exploring information from multiple views, one c
Externí odkaz:
https://doaj.org/article/728200e0f4d34450904aebd26bda1a4f
Autor:
Cong-Zhe You, Xiao-Jun Wu
Publikováno v:
Journal of Algorithms & Computational Technology, Vol 9 (2015)
In order to get better clustering precision, the traditional clustering algorithms usually need the support of large amount of historical data. The impact it brings about is: the previous clustering algorithm seems not effective if there exists some
Externí odkaz:
https://doaj.org/article/acbed2f4065143cab9a28e5261419948
Publikováno v:
Engineering Applications of Artificial Intelligence. 77:117-124
Subspace clustering algorithms are usually used when processing high-dimensional data, such as in computer vision. This paper presents a robust low-rank representation (LRR) method that incorporates structure constraints and dimensionality reduction
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::92ec9145389ada1b05debae42636462a
https://doi.org/10.1016/j.engappai.2018.09.008
https://doi.org/10.1016/j.engappai.2018.09.008
Publikováno v:
2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES).
Due to the excellent performance in exploring the structure of low-dimensional subspaces, the low-rank representation (LRR) has recently attracted wide attention of the researchers. However, in most current semi-supervised learning problems based on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f85dd7ee9b05caf158744f1b40d3e2d8
https://doi.org/10.1109/dcabes.2018.00033
https://doi.org/10.1109/dcabes.2018.00033