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» Data Clustering with Partial Supervision
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TNN
2010
155views Management» more  TNN 2010»
13 years 4 months ago
Incorporating the loss function into discriminative clustering of structured outputs
Clustering using the Hilbert Schmidt independence criterion (CLUHSIC) is a recent clustering algorithm that maximizes the dependence between cluster labels and data observations ac...
Wenliang Zhong, Weike Pan, James T. Kwok, Ivor W. ...
VC
2008
118views more  VC 2008»
13 years 9 months ago
Enriching a motion database by analogous combination of partial human motions
We synthesize new human body motions from existing motion data. We divide the body of an animated character into several parts, such as to upper and lower body, and partition the m...
Won-Seob Jang, Won-Kyu Lee, In-Kwon Lee, Jehee Lee
IDA
2002
Springer
13 years 9 months ago
Evolutionary model selection in unsupervised learning
Feature subset selection is important not only for the insight gained from determining relevant modeling variables but also for the improved understandability, scalability, and pos...
YongSeog Kim, W. Nick Street, Filippo Menczer
SIGIR
2002
ACM
13 years 9 months ago
Unsupervised document classification using sequential information maximization
We present a novel sequential clustering algorithm which is motivated by the Information Bottleneck (IB) method. In contrast to the agglomerative IB algorithm, the new sequential ...
Noam Slonim, Nir Friedman, Naftali Tishby
FGR
2008
IEEE
214views Biometrics» more  FGR 2008»
14 years 4 months ago
Normalized LDA for semi-supervised learning
Linear Discriminant Analysis (LDA) has been a popular method for feature extracting and face recognition. As a supervised method, it requires manually labeled samples for training...
Bin Fan, Zhen Lei, Stan Z. Li