Results 81 to 90 of about 9,565 (204)
Subspace clustering with dense representations
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013 2013 IEEE International Conference on Acoustics, Speech and Signal ...
Dyer, Eva L. +2 more
openaire +2 more sources
Cluster Evaluation of Density Based Subspace Clustering
, 2010 Clustering real world data often faced with curse of dimensionality, where real world data often consist of many dimensions. Multidimensional data clustering evaluation can be done through a density-based approach.
Jasni, Mohamad Zain +1 more
core
Hierarchical Subspace Clustering [PDF]
, 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.
Elke Achtert, Achtert, Elke
core
Subspace Clustering Techniques
, 2017 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.
Arthur Zimek +3 more
core +1 more source
Deep subspace clustering networks [PDF]
, 2020 We present a novel deep neural network architecture for unsupervised subspace clustering. This architecture is built upon deep auto-encoders, which non-linearly map the input data into a latent space.
Salzmann, Mathieu +4 more
core
Tensioned Multi-View Ordered Kernel Subspace Clustering
Applied SciencesMulti-view data improve the effectiveness of clustering tasks, but they often encounter complex noise and corruption. The missing view of the multi-view samples leads to serious degradation of the clustering model’s performance.
Liping Chen, Gongde Guo
doaj +1 more source
Low-Rank Tensor Thresholding Ridge Regression
IEEE Access, 2019 In the area of subspace clustering, methods combining self-representation and spectral clustering are predominant in recent years. For dealing with tensor data, most existing methods vectorize them into vectors and lose most of the spatial information ...
Kailing Guo +3 more
doaj +1 more source
A survey on enhanced subspace clustering
, 2013 Subspace clustering finds sets of objects that are homogeneous in subspaces of high-dimensional datasets, and has been successfully applied in many domains.
Gopalkrishnan, Vivekanand +3 more
core +1 more source
Subspace discovery for video anomaly detection
, 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
Connectedness-based subspace clustering
, 2018 An algorithm for density-based subspace clustering of given data is proposed here. Unlike the existing density-based subspace clustering algorithms which find clusters using spatial proximity, existence of common high-density regions is the condition for
C. A. Murthy +3 more
core +1 more source