Results 261 to 270 of about 541,400 (289)

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
openaire   +2 more sources

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
openaire   +1 more source

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
openaire   +1 more source

Finding the Optimal Subspace for Clustering

2014 IEEE International Conference on Data Mining, 2014
The ability to simplify and categorize things is one of the most important elements of human thought, understanding, and learning. The corresponding explorative data analysis techniques - dimensionality reduction and clustering - have initially been studied by our community as two separate research topics. Later algorithms like CLIQUE, ORCLUS, 4C, etc.
Goebl, S., He, X., Mai, S.T., Plant, C., Böhm, C.   +4 more
openaire   +4 more sources

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
openaire   +3 more sources

Random spatial subspace clustering

Knowledge-Based Systems, 2015
Strong spatial or time correlation exists in many types of data, for example, the hyperspectral data acquired by a spectrometer scanning through rock samples from a drill hole. It is of practical interests to identify spatially continuous segments in a given data set where we know a priori that the samples are strongly correlated spatially. Recently, a
Yi Guo 0001, Junbin Gao, Feng Li 0003
openaire   +2 more sources

Evolving soft subspace clustering

Applied Soft Computing, 2014
A key challenge to most conventional clustering algorithms in handling many real world problems is that, data points in different clusters are often correlated with different subsets of features. To address this problem, subspace clustering has attracted increasing attention in recent years. In practical data mining applications, data points may arrive
Lin Zhu, Longbing Cao, Jie Yang 0002, Jingsheng Lei   +3 more
openaire   +2 more sources

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
openaire   +4 more sources

Iterative Evolutionary Subspace Clustering

2012
We propose in this paper a subspace clustering in high dimensional datasets using an iterative evolutionary algorithm. The evolutionary algorithm offers an original alternative to solve the problem of selecting subspace to deal with complex data structures in different subspaces.
Lydia Boudjeloud-Assala, Alexandre Blansché   +1 more
openaire   +2 more sources

Consensus Subspace Clustering

2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), 2021
Nathan Thom, Hung Nguyen 0005, Emily M. Hand   +2 more
openaire   +2 more sources