Results 11 to 20 of about 9,565 (204)
Projection subspace clustering
Journal of Algorithms & Computational Technology, 2017 Gene expression data is a kind of high dimension and small sample size data. The clustering accuracy of conventional clustering techniques is lower on gene expression data due to its high dimension.
Xiaoyun Chen, Mengzhen Liao, Xianbao Ye
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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.
Jyoti Maggu +3 more
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Discriminative Subspace Clustering [PDF]
2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013 We present a novel method for clustering data drawn from a union of arbitrary dimensional subspaces, called Discriminative Subspace Clustering (DiSC). DiSC solves the subspace clustering problem by using a quadratic classifier trained from unlabeled data (clustering by classification).
Vasileios Zografos +2 more
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Clustering quality metrics for subspace clustering
Pattern Recognition, 2020 Abstract We study the problem of clustering validation, i.e., clustering evaluation without knowledge of ground-truth labels, for the increasingly-popular framework known as subspace clustering. Existing clustering quality metrics (CQMs) rely heavily on a notion of distance between points, but common metrics fail to capture the geometry of subspace ...
John Lipor, Laura Balzano
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The Generic Subspace Clustering Model [PDF]
, 2010 In this paper we present an overview of methods for clustering high dimensional data in which the objects are assigned to mutually exclusive classes in low dimensional spaces. To this end, we will introduce the generic subspace clustering model. This model will be shown to encompass a range of existing clustering techniques as special cases.
Marieke E. Timmerman, Eva Ceulemans
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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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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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Multi-Partitions Subspace Clustering
Mathematics, 2020 In model based clustering, it is often supposed that only one clustering latent variable explains the heterogeneity of the whole dataset. However, in many cases several latent variables could explain the heterogeneity of the data at hand.
Vincent Vandewalle
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