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» An Adaptive Kernel Method for Semi-supervised Clustering
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TKDE
2012
270views Formal Methods» more  TKDE 2012»
11 years 7 months ago
Low-Rank Kernel Matrix Factorization for Large-Scale Evolutionary Clustering
—Traditional clustering techniques are inapplicable to problems where the relationships between data points evolve over time. Not only is it important for the clustering algorith...
Lijun Wang, Manjeet Rege, Ming Dong, Yongsheng Din...
ICPR
2006
IEEE
14 years 6 months ago
Adaptive Feature Integration for Segmentation of 3D Data by Unsupervised Density Estimation
In this paper, a novel unsupervised approach for the segmentation of unorganized 3D points sets is proposed. The method derives by the mean shift clustering paradigm devoted to se...
Marco Cristani, Umberto Castellani, Vittorio Murin...
CIKM
2006
Springer
13 years 9 months ago
Adaptive non-linear clustering in data streams
Data stream clustering has emerged as a challenging and interesting problem over the past few years. Due to the evolving nature, and one-pass restriction imposed by the data strea...
Ankur Jain, Zhihua Zhang, Edward Y. Chang
ICASSP
2011
IEEE
12 years 9 months ago
Adaptive N-normalization for enhancing music similarity
The N-Normalization is an efficient method for normalizing a given similarity computed among multimedia objects. It can be considered for clustering and kernel enhancement. Howev...
Mathieu Lagrange, George Tzanetakis
IJON
2006
109views more  IJON 2006»
13 years 5 months ago
Integrating the improved CBP model with kernel SOM
In this paper, we first design a more generalized network model, Improved CBP, based on the same structure as Circular BackPropagation (CBP) proposed by Ridella et al. The novelty ...
Qun Dai, Songcan Chen