Results 171 to 180 of about 9,565 (204)

Efficient dense subspace clustering

IEEE Winter Conference on Applications of Computer Vision, 2014
In this paper, we tackle the problem of clustering data points drawn from a union of linear (or affine) subspaces. To this end, we introduce an efficient subspace clustering algorithm that estimates dense connections between the points lying in the same subspace.
Pan Ji, Mathieu Salzmann, Hongdong Li
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Subspace clustering

WIREs Data Mining and Knowledge Discovery, 2012
AbstractSubspace clusteringrefers to the task of identifying clusters of similar objects or data records (vectors) where the similarity is defined with respect to a subset of the attributes (i.e., a subspace of the data space). The subspace is not necessarily (and actually is usually not) the same for different clusters within one clustering solution ...
Hans-Peter Kriegel, Peer Kröger, Arthur Zimek   +2 more
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Predictive Subspace Clustering

2011 10th International Conference on Machine Learning and Applications and Workshops, 2011
The problem of detecting clusters in high-dimensional data is increasingly common in machine learning applications, for instance in computer vision and bioinformatics. Recently, a number of approaches in the field of subspace clustering have been proposed which search for clusters in subspaces of unknown dimensions. Learning the number of clusters, the
Brian McWilliams, Giovanni Montana
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Comparing subspace clusterings

IEEE Transactions on Knowledge and Data Engineering, 2006
We present the first framework for comparing subspace clusterings. We propose several distance measures for subspace clusterings, including generalizations of well-known distance measures for ordinary clusterings. We describe a set of important properties for any measure for comparing subspace clusterings and give a systematic comparison of our ...
Anne Patrikainen, Marina Meila
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Cascade Subspace Clustering

Proceedings of the AAAI Conference on Artificial Intelligence, 2017
In this paper, we recast the subspace clustering as a verification problem. Our idea comes from an assumption that the distribution between a given sample x and cluster centers Omega is invariant to different distance metrics on the manifold, where each distribution is defined as a probability map (i.e.
Xi Peng 0001, Jiashi Feng, Jiwen Lu, Wei-Yun Yau, Zhang Yi 0001   +4 more
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Nonlinear subspace clustering

2017 IEEE International Conference on Image Processing (ICIP), 2017
This paper presents a nonlinear subspace clustering (NSC) method for image clustering. Unlike most existing subspace clustering methods which only exploit the linear relationship of samples to learn the affine matrix, our NSC reveals the multi-cluster nonlinear structure of samples via a nonlinear neural network.
Wencheng Zhu, Jiwen Lu, Jie Zhou 0001
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Subspace Structure-Aware Spectral Clustering for Robust Subspace Clustering

2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019
Subspace clustering is the problem of partitioning data drawn from a union of multiple subspaces. The most popular subspace clustering framework in recent years is the graph clustering-based approach, which performs subspace clustering in two steps: graph construction and graph clustering.
Masataka Yamaguchi, Go Irie, Takahito Kawanishi, Kunio Kashino   +3 more
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Interpretable Subspace Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Subspace clustering is one of the most popular clustering methods due to its effectiveness. Although subspace clustering methods have been demonstrated to achieve promising performance, they still lack interpretability, especially when handling high-dimensional complicated data. To bridge this gap, this paper focuses on the interpretability of subspace
Zheng Zhang, Peng Zhou 0006, Aiting Yao, Liang Du 0003, Xinwang Liu 0002   +4 more
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Nonlinear subspace clustering for image clustering

Pattern Recognition Letters, 2018
Abstract We present in this paper a nonlinear subspace clustering (NSC) method for image clustering. Unlike most existing subspace clustering methods which only exploit the linear relationship of samples to learn the affine matrix, our NSC reveals the multi-cluster nonlinear structure of samples via a nonlinear neural network.
Wencheng Zhu, Jiwen Lu, Jie Zhou 0001
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Clustering Curves on a Reduced Subspace

Journal of Computational and Graphical Statistics, 2012
The aim of this article is to propose a procedure to cluster functional observations in a subspace of reduced dimension. The dimensional reduction is obtained by constraining the cluster centroids to lie into a subspace which preserves the maximum amount of discriminative information contained in the original data.
GATTONE, STEFANO ANTONIO, ROCCI, ROBERTO
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