Results 11 to 20 of about 541,400 (289)
Projection subspace clustering
Journal of Algorithms & Computational Technology, 2017 Gene expression data is a kind of high dimension and small sample size data. The clustering accuracy of conventional clustering techniques is lower on gene expression data due to its high dimension.
Xiaoyun Chen, Mengzhen Liao, Xianbao Ye
doaj +2 more sources
Deeply transformed subspace clustering [PDF]
Signal Processing, 2020 Subspace clustering assumes that the data is separable into separate subspaces; this assumption may not always hold. For such cases, we assume that, even if the raw data is not separable into subspaces, one can learn a deep representation such that the learnt representation is separable into subspaces.
Jyoti Maggu +3 more
openaire +4 more sources
Discriminative Subspace Clustering [PDF]
2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013 We present a novel method for clustering data drawn from a union of arbitrary dimensional subspaces, called Discriminative Subspace Clustering (DiSC). DiSC solves the subspace clustering problem by using a quadratic classifier trained from unlabeled data (clustering by classification).
Vasileios Zografos +2 more
openaire +3 more sources
The Generic Subspace Clustering Model [PDF]
, 2010 In this paper we present an overview of methods for clustering high dimensional data in which the objects are assigned to mutually exclusive classes in low dimensional spaces. To this end, we will introduce the generic subspace clustering model. This model will be shown to encompass a range of existing clustering techniques as special cases.
Marieke E. Timmerman, Eva Ceulemans
openaire +4 more sources
Subspace discovery for video anomaly detection [PDF]
, 2010 PhDIn automated video surveillance anomaly detection is a challenging task. We address this task as a novelty detection problem where pattern description is limited and labelling information is available only for a small sample of normal instances.
Tziakos, Ioannis
core +4 more sources
MethyAnno: An Interpretable Automated Annotation Method Leveraging Multi-Scale Information and Metric Learning Framework for scDNAm Data. [PDF]
Adv Sci (Weinh)MethyAnno enables robust and interpretable annotation of single‐cell DNA methylation data by integrating multi‐scale epigenetic information, bidirectional cross‐attention, and prototype‐based metric learning. The framework resolves rare and novel cell types across datasets while revealing cell‐type‐specific epigenetic signatures associated with disease
Jia Y, Li S, Tang S, Gu K, Chen S.
europepmc +2 more sources
Orderly Subspace Clustering [PDF]
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 Semi-supervised representation-based subspace clustering is to partition data into their underlying subspaces by finding effective data representations with partial supervisions. Essentially, an effective and accurate representation should be able to uncover and preserve the true data structure.
Jing Wang 0023 +5 more
openaire +3 more sources
Attributed Subspace Clustering [PDF]
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019 Existing methods on representation-based subspace clustering mainly treat all features of data as a whole to learn a single self-representation and get one clustering solution. Real data however are often complex and consist of multiple attributes or sub-features, such as a face image has expressions or genders.
Jing Wang 0023 +5 more
openaire +2 more sources
A rough set based subspace clustering technique for high dimensional data
Journal of King Saud University: Computer and Information Sciences, 2020 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.
B. Jaya Lakshmi, M. Shashi, K.B. Madhuri
doaj +1 more source
A Streamlined scRNA-Seq Data Analysis Framework Based on Improved Sparse Subspace Clustering
IEEE Access, 2021 One advantage of single-cell RNA sequencing is its ability in revealing cell heterogeneity by cell clustering. However, cell clustering based on single-cell RNA sequencing is challenging due to the high transcript amplification noise, sparsity and ...
Jujuan Zhuang +7 more
doaj +1 more source