Results 11 to 20 of about 49,338 (223)
A Fault-tolerance Subspace Clustering Algorithm in Data Mining [PDF]
Jisuanji gongcheng, 2016 In order to improve the computation efficiency of the subspace clustering algorithm,this paper gives a general fault-tolerance subspace clustering definition according to the number of objects,dimensions,mode tolerance and relative threshold constraint ...
TIAN Jinhua,SUN Li
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Pseudo-Supervised Deep Subspace Clustering [PDF]
IEEE Transactions on Image Processing, 2021 Auto-Encoder (AE)-based deep subspace clustering (DSC) methods have achieved impressive performance due to the powerful representation extracted using deep neural networks while prioritizing categorical separability. However, self-reconstruction loss of an AE ignores rich useful relation information and might lead to indiscriminative representation ...
Juncheng Lv +3 more
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Attributed Subspace Clustering [PDF]
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019 Existing methods on representation-based subspace clustering mainly treat all features of data as a whole to learn a single self-representation and get one clustering solution. Real data however are often complex and consist of multiple attributes or sub-features, such as a face image has expressions or genders.
Wang, J. +5 more
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Orderly Subspace Clustering [PDF]
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 Semi-supervised representation-based subspace clustering is to partition data into their underlying subspaces by finding effective data representations with partial supervisions. Essentially, an effective and accurate representation should be able to uncover and preserve the true data structure.
Wang, J. +5 more
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Deeply transformed subspace clustering [PDF]
Signal Processing, 2020 Subspace clustering assumes that the data is separable into separate subspaces; this assumption may not always hold. For such cases, we assume that, even if the raw data is not separable into subspaces, one can learn a deep representation such that the learnt representation is separable into subspaces.
Maggu, Jyoti +3 more
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A rough set based subspace clustering technique for high dimensional data
Journal of King Saud University: Computer and Information Sciences, 2020 Subspace clustering aims at identifying subspaces for cluster formation so that the data is categorized in different perspectives. The conventional subspace clustering algorithms explore dense clusters in all the possible subspaces.
B. Jaya Lakshmi, M. Shashi, K.B. Madhuri
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A Streamlined scRNA-Seq Data Analysis Framework Based on Improved Sparse Subspace Clustering
IEEE Access, 2021 One advantage of single-cell RNA sequencing is its ability in revealing cell heterogeneity by cell clustering. However, cell clustering based on single-cell RNA sequencing is challenging due to the high transcript amplification noise, sparsity and ...
Jujuan Zhuang +7 more
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An Overlapping Subspace K-Means Clustering Algorithm [PDF]
Jisuanji gongcheng, 2020 Most of existing clustering algorithms for high-dimensional sparse data do not consider overlapping class clusters and outliers,resulting in unsatisfactory clustering results.Therefore,this paper proposes an overlapping subspace K-Means clustering ...
LIU Yuhang, MA Huifang, LIU Haijiao, YU Li
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Kernel Truncated Regression Representation for Robust Subspace Clustering [PDF]
, 2020 Subspace clustering aims to group data points into multiple clusters of which each corresponds to one subspace. Most existing subspace clustering approaches assume that input data lie on linear subspaces.
Peng, Dezhong +3 more
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Remote Sensing, 2022 Clustering of multi-source geospatial big data provides opportunities to comprehensively describe urban structures. Most existing studies focus only on the clustering of a single type of geospatial big data, which leads to biased results.
Qiliang Liu, Weihua Huan, Min Deng
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