Results 251 to 260 of about 541,400 (289)

Free Energy Calculation Method Based on Enhanced Sampling of Diverse Protein Conformations Predicted by Artificial Intelligence. [PDF]

J Phys Chem Lett
Aoki T, Harada R.
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Cytokine-induced chromatin accessibility in whole blood neutrophils links to sepsis transcriptional states. [PDF]

Front Immunol
Cayford J, Atteberry B, Singh-Taylor A, Retter A, Berman BP, Kelly TK.   +5 more
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CiCLoDS: Joint cell clustering and gene selection for single-cell spatial transcriptomics. [PDF]

Sci Rep
Wang N, He Y, Ray E, Won KJ, Cetrulo CL, Li D, Moore JH, Zhang S, Huang X.   +8 more
europepmc   +1 more source

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Subspace Clustering

IEEE Signal Processing Magazine, 2011
René Vidal
exaly   +4 more sources

Generalized Independent Subspace Clustering

2016 IEEE 16th International Conference on Data Mining (ICDM), 2016
Data can encapsulate different object groupings in subspaces of arbitrary dimension and orientation. Finding such subspaces and the groupings within them is the goal of generalized subspace clustering. In this work we present a generalized subspace clustering technique capable of finding multiple non-redundant clusterings in arbitrarily-oriented ...
Wei Ye 0001, Samuel Maurus, Nina C. Hubig, Claudia Plant   +3 more
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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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Deep Subspace Clustering

IEEE Transactions on Neural Networks and Learning Systems, 2020
In this article, we propose a deep extension of sparse subspace clustering, termed deep subspace clustering with L1-norm (DSC-L1). Regularized by the unit sphere distribution assumption for the learned deep features, DSC-L1 can infer a new data affinity matrix by simultaneously satisfying the sparsity principle of SSC and the nonlinearity given by ...
Xi Peng 0001, Jiashi Feng, Joey Tianyi Zhou, Yingjie Lei, Shuicheng Yan   +4 more
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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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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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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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