Results 51 to 60 of about 541,400 (289)
Soft Subspace Clustering Algorithm Optimized by Brain Storm Algorithm for Breast MR Image
Jisuanji kexue yu tansuo, 2020 The traditional soft subspace clustering algorithm is very susceptible to the initial clustering center and noise data when segmenting breast MR images with large amount of information, uneven intensity and boundary blur, which results in that algorithm ...
FAN Hong, SHI Xiaomin, YAO Ruoxia
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Kernel Block Diagonal Representation Subspace Clustering with Similarity Preservation
Applied Sciences, 2023 Subspace clustering methods based on the low-rank and sparse model are effective strategies for high-dimensional data clustering. However, most existing low-rank and sparse methods with self-expression can only deal with linear structure data effectively,
Yifang Yang, Fei Li
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IET Radar, Sonar & Navigation, 2022 In the field of radar reconnaissance, unsupervised recognition of radar signals is a particularly important method for classifying different signals and estimating signal parameters.
Lutao Liu, Shuai Xu
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Subspace Clustering of Subspaces: Unifying Canonical Correlation Analysis and Subspace Clustering
IEEE Transactions on Signal Processing19 pages, Submitted to IEEE Transactions on Signal ...
Paris A. Karakasis +1 more
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Fuzzy Subspace Clustering [PDF]
, 2009 In clustering we often face the situation that only a subset of the available attributes is relevant for forming clusters, even though this may not be known beforehand. In such cases it is desirable to have a clustering algorithm that automatically weights attributes or even selects a proper subset.
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SSC: Statistical Subspace Clustering [PDF]
, 2005 Subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces within a dataset. This is a particularly important challenge with high dimensional data where the curse of dimensionality occurs. It has also the benefit of providing smaller descriptions of the clusters found. Existing methods only consider
Candillier, Laurent +3 more
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An Efficient Representation-Based Subspace Clustering Framework for Polarized Hyperspectral Images
Remote Sensing, 2019 Recently, representation-based subspace clustering algorithms for hyperspectral images (HSIs) have been developed with the assumption that pixels belonging to the same land-cover class lie in the same subspace. Polarization is regarded to be a complement
Zhengyi Chen +5 more
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Multi-view subspace clustering
, 2016 For many computer vision applications, the data sets distribute on certain low;dimensional subspaces. Subspace clustering is to find such underlying subspaces and cluster the data points correctly.
Gao, Hongchang (hongchanggao@gmail.com) +4 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Recently, a series of collaborative representation (CR) methods have attracted much attention for hyperspectral images classification. In this article, two CR-based dynamic ensemble selection (DES) methods using multiview kernel collaborative subspace ...
Hongliang Lu +3 more
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Advanced Functional Materials, EarlyView.ABSTRACT Neat films of organic dyes are an interesting technological platform for solar cells and photodetectors. Recently, the conventional biphasic bulk‐heterojunction paradigm for photoinduced charge separation was challenged by the observation that charge carriers can be photogenerated in single‐component materials. A comprehensive experimental and
Davide Giavazzi +12 more
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