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» Sparse Unsupervised Dimensionality Reduction Algorithms
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ICANN
2003
Springer
14 years 23 days ago
Supervised Locally Linear Embedding
Locally linear embedding (LLE) is a recently proposed method for unsupervised nonlinear dimensionality reduction. It has a number of attractive features: it does not require an ite...
Dick de Ridder, Olga Kouropteva, Oleg Okun, Matti ...
KDD
2005
ACM
118views Data Mining» more  KDD 2005»
14 years 8 months ago
On the use of linear programming for unsupervised text classification
We propose a new algorithm for dimensionality reduction and unsupervised text classification. We use mixture models as underlying process of generating corpus and utilize a novel,...
Mark Sandler
PRL
2006
121views more  PRL 2006»
13 years 7 months ago
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Data reduction plays an important role in machine learning and pattern recognition with a high-dimensional data. In real-world applications data usually exists with hybrid formats...
Qinghua Hu, Daren Yu, Zongxia Xie
PR
2011
12 years 10 months ago
A survey of multilinear subspace learning for tensor data
Increasingly large amount of multidimensional data are being generated on a daily basis in many applications. This leads to a strong demand for learning algorithms to extract usef...
Haiping Lu, Konstantinos N. Plataniotis, Anastasio...
ICCV
2007
IEEE
14 years 9 months ago
Real-time Body Tracking Using a Gaussian Process Latent Variable Model
In this paper, we present a tracking framework for capturing articulated human motions in real-time, without the need for attaching markers onto the subject's body. This is a...
Shaobo Hou, Aphrodite Galata, Fabrice Caillette, N...