Results 101 to 110 of about 9,565 (204)

Local Feature Discriminant Projection

, 2016
In this paper, we propose a novel subspace learning algorithm called Local Feature Discriminant Projection (LFDP) for supervised dimensionality reduction of local features.
Zhen, Xiantong, Shao, Ling, He, Xiaofei, Yu, Mengyang   +3 more
core   +1 more source

Deciphering the Immune Complexity in Esophageal Adenocarcinoma and Pre-Cancerous Lesions With Sequential Multiplex Immunohistochemistry and Sparse Subspace Clustering Approach. [PDF]

Front Immunol, 2022
Sundaram S, Kim EN, Jones GM, Sivagnanam S, Tripathi M, Miremadi A, Di Pietro M, Coussens LM, Fitzgerald RC, Chang YH, Zhuang L.   +10 more
europepmc   +1 more source

Neural collaborative subspace clustering

, 2022
We introduce the Neural Collaborative Subspace Clustering, a neural model that discovers clusters of data points drawn from a union of lowdimensional subspaces.
Harandi, Mehrtash, Zhang, Tong, Li, Hongdong, Ji, Pan, Huang, Wenbing   +4 more
core  

Differentially Private Subspace Clustering [PDF]

, 2020
Subspace clustering is an unsupervised learning problem that aims at grouping data points into multiple "clusters" so that data points in a single cluster lie approximately on a low-dimensional linear subspace.
Aarti Singh, Yu-Xiang Wang, Yining Wang
core  

Subspace Clustering and Active Learning with Constraints [PDF]

, 2020
Data representations can often be high-dimensional, whether it is due to the large number of collected / recorded features or due to how the data sources (e.g. images, texts) are processed.
Peng, Hankui, Pavlidis, Nicos, Eckley, Idris   +2 more
core  

Evaluating subspace clustering algorithms

, 2004
Clustering techniques often define the similarity between instances using distance measures over the various dimensions of the data [12, 14]. Subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces
Lance Parsons
core  

Morpheus: Interactive Exploration of Subspace Clustering

, 2008
Data mining techniques extract interesting patterns out of large data resources. Meaningful visualization and interactive exploration of patterns are crucial for knowledge discovery.
Müller, Emmanuel Alexander, Assent, Ira, Timm Jansen, Müller, Emmanuel, Seidl, Thomas, Thomas Seidl, Krieger, Ralph, Ira Assent, Jansen, Timm, Emmanuel Müller, Ralph Krieger   +10 more
core   +1 more source

Subspace Clustering for Sequential Data

, 2014
We propose Ordered Subspace Clustering (OSC) to seg-ment data drawn from a sequentially ordered union of sub-spaces. Current subspace clustering techniques learn the relationships within a set of data and then use a separate clustering algorithm such as ...
Junbin Gao, Yi Guo, Guo, Yi (R18457), Stephen Tierney, Gao, Junbin, Tierney, Stephen   +5 more
core   +1 more source

Subspace Clustering for all Seasons [PDF]

, 2015
International audienceSubspace clustering is recognized as a more general and difficult task than standard clustering since it requires to identify not only objects sharing similar feature values but also the various subspaces where these similarities ...
Peignier, Sergio, Beslon, Guillaume, Rigotti, Christophe   +2 more
core   +2 more sources

Subspace Clustering of Physiological Data From Acute Traumatic Brain Injury Patients: Retrospective Analysis Based on the PROTECT III Trial. [PDF]

JMIR Biomed Eng, 2021
Ehsani S, Reddy CK, Foreman B, Ratcliff J, Subbian V.   +4 more
europepmc   +1 more source