Results 261 to 270 of about 1,461,923 (295)

Spectral sparsification in spectral clustering

2016 23rd International Conference on Pattern Recognition (ICPR), 2016
Graph spectral clustering algorithms have been shown to be effective in finding clusters and generally outperform traditional clustering algorithms, such as k-means. However, they have scalibility issues in both memory usage and computational time. To overcome these limitations, the common approaches sparsify the similarity matrix by zeroing out some ...
Alireza Chakeri, Hamidreza Farhidzadeh, Lawrence O. Hall   +2 more
openaire   +1 more source

Correlational spectral clustering

2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008
We present a new method for spectral clustering with paired data based on kernel canonical correlation analysis, called correlational spectral clustering. Paired data are common in real world data sources, such as images with text captions. Traditional spectral clustering algorithms either assume that data can be represented by a single similarity ...
Matthew B. Blaschko, Christoph H. Lampert   +1 more
openaire   +3 more sources

Spectral Cluster Maps Versus Spectral Clustering

2020
The paper investigates several notions of graph Laplacians and graph kernels from the perspective of understanding the graph clustering via the graph embedding into an Euclidean space. We propose hereby a unified view of spectral graph clustering and kernel clustering methods.
Slawomir T. Wierzchon, Mieczyslaw A. Klopotek   +1 more
openaire   +2 more sources

Robust path-based spectral clustering

Pattern Recognition, 2008
Spectral clustering and path-based clustering are two recently developed clustering approaches that have delivered impressive results in a number of challenging clustering tasks. However, they are not robust enough against noise and outliers in the data.
Dit-Yan Yeung
exaly   +2 more sources

Streaming spectral clustering

2016 IEEE 32nd International Conference on Data Engineering (ICDE), 2016
Clustering is a classical data mining task used for discovering interrelated pattern of similarities in the data. In many modern day domains, data is getting continuously generated as a stream. For scalability reasons, clustering the points in a data stream requires designing single pass, limited memory streaming clustering algorithms.
Shinjae Yoo, Hao Huang 0007, Shiva Prasad Kasiviswanathan   +2 more
openaire   +2 more sources

Spectral Clustering by Joint Spectral Embedding and Spectral Rotation [PDF]

IEEE Transactions on Cybernetics, 2020
Spectral clustering is an important clustering method widely used for pattern recognition and image segmentation. Classical spectral clustering algorithms consist of two separate stages: 1) solving a relaxed continuous optimization problem to obtain a real matrix followed by 2) applying K -means or spectral rotation to round the real matrix (i.e ...
Yanwei Pang, Jin Xie, Xuelong Li
exaly   +4 more sources

Spectral Ensemble Clustering

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015
Ensemble clustering, also known as consensus clustering, is emerging as a promising solution for multi-source and/or heterogeneous data clustering. The co-association matrix based method, which redefines the ensemble clustering problem as a classical graph partition problem, is a landmark method in this area.
Hongfu Liu 0001, Tongliang Liu, Junjie Wu 0002, Dacheng Tao, Yun Fu 0001   +4 more
openaire   +2 more sources

Compressed Spectral Clustering

2009 IEEE International Conference on Data Mining Workshops, 2009
Compressed sensing has received much attention in both data mining and signal processing communities. In this paper, we provide theoretical results to show that compressed spectral clustering, separating data samples into different clusters directly in the compressed measurement domain, is possible.
Bin Zhao 0004, Changshui Zhang
openaire   +1 more source

Approximate Spectral Clustering

2009
While spectral clustering has recently shown great promise, computational cost makes it infeasible for use with large data sets. To address this computational challenge, this paper considers the problem of approximate spectral clustering, which enables both the feasibility (of approximately clustering in very large and unloadable data sets) and ...
Liang Wang 0001, Christopher Leckie, Kotagiri Ramamohanarao, James C. Bezdek   +3 more
openaire   +2 more sources

Spectral face clustering

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, 2009
Recognizing and clustering similar faces are important for organizing digital photos. We present a novel clustering method based on spectral clustering to group faces in a photo album. The main contribution is the proposal of a distance metric that is robust to outlier features present in the facial images.
Biswaroop Palit, Rakesh Nigam, Keren Perlmutter, Sharon M. Perlmutter   +3 more
openaire   +2 more sources