Results 211 to 220 of about 49,338 (223)

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
openaire   +1 more source

Tensor Subspace Cluster

2021
As a typical unsupervised learning technique, subspace clustering learns the subspaces of data and assigns data into their respective subspaces, which is important for a number of data processing applications. Traditional subspace clustering is based on matrix computation, and it is inevitable to lose some structural information when dealing with ...
Yipeng Liu, Jiani Liu, Zhen Long, Ce Zhu
openaire   +1 more source

Subspace clustering through attribute clustering

Frontiers of Electrical and Electronic Engineering in China, 2008
Many recently proposed subspace clustering methods suffer from two severe problems. First, the algorithms typically scale exponentially with the data dimensionality or the subspace dimensionality of clusters. Second, the clustering results are often sensitive to input parameters.
Kun Niu, Shubo Zhang, Junliang Chen
openaire   +1 more source

Subspace correlation clustering

Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012
The necessity to analyze subspace projections of complex data is a well-known fact in the clustering community. While the full space may be obfuscated by overlapping patterns and irrelevant dimensions, only certain subspaces are able to reveal the clustering structure.
Stephan Günnemann, Ines Färber, Kittipat Virochsiri, Thomas Seidl   +3 more
openaire   +1 more source

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
openaire   +1 more source

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 ...
A. Patrikainen, M. Meila
openaire   +1 more source

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, Jiashi Feng, Jiwen Lu, Wei-Yun Yau, Zhang Yi   +4 more
openaire   +1 more source

Hierarchical Subspace Clustering

2007
It is well-known that traditional clustering methods considering all dimensions of the feature space usually fail in terms of efficiency and effectivity when applied to high-dimensional data. This poor behavior is based on the fact that clusters may not be found in the high-dimensional feature space, although clusters exist in subspaces of the feature ...
openaire   +1 more source

Subspace Clustering

IEEE Signal Processing Magazine, 2011
openaire   +2 more sources

Consensus Subspace Clustering

2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), 2021
Nathan Thom, Hung Nguyen, Emily M. Hand
openaire   +1 more source