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SGCRNA: spectral clustering-guided co-expression network analysis without scale-free constraints for multi-omic data. [PDF]

Brief Bioinform
Osone T, Takao T, Otake S, Takarada T.
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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, Jiashi Feng, Joey Tianyi Zhou, Yingjie Lei, Shuicheng Yan   +4 more
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Multiview Subspace Dual Clustering

IEEE Transactions on Neural Networks and Learning Systems, 2022
A single clustering refers to the partitioning of data such that the similar data are assigned into the same group, whereas the dissimilar data are separated into different groups. Recently, multiview clustering has received significant attention in recent years.
Shirui Luo, Xiaochun Cao
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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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Subspace K-means clustering

Behavior Research Methods, 2013
To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic,
Timmerman, Marieke E., Ceulemans, Eva, De Roover, Kim, Van Leeuwen, Karla   +3 more
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Information Theoretic Subspace Clustering

IEEE Transactions on Neural Networks and Learning Systems, 2016
This paper addresses the problem of grouping the data points sampled from a union of multiple subspaces in the presence of outliers. Information theoretic objective functions are proposed to combine structured low-rank representations (LRRs) to capture the global structure of data and information theoretic measures to handle outliers.
Ran, He, Liang, Wang, Zhenan, Sun, Yingya, Zhang, Bo, Li   +4 more
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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 ...
Ye, W., Maurus, S., Hubig, N., Plant, C.
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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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Subspace Clustering Techniques

2009
Subspace clustering aims at identifying subspaces for cluster formation so that the data is categorized in different perspectives. The conventional subspace clustering algorithms explore dense clusters in all the possible subspaces. These algorithms suffer from the curse of dimensionality.
Kröger, Peer, Zimek, Arthur
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