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» Supervised dimensionality reduction using mixture models
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SIGIR
2005
ACM
15 years 5 months ago
Orthogonal locality preserving indexing
We consider the problem of document indexing and representation. Recently, Locality Preserving Indexing (LPI) was proposed for learning a compact document subspace. Different from...
Deng Cai, Xiaofei He
GRC
2010
IEEE
15 years 22 days ago
Learning Multiple Latent Variables with Self-Organizing Maps
Inference of latent variables from complicated data is one important problem in data mining. The high dimensionality and high complexity of real world data often make accurate infe...
Lili Zhang, Erzsébet Merényi
BMCBI
2006
202views more  BMCBI 2006»
14 years 11 months ago
Spectral embedding finds meaningful (relevant) structure in image and microarray data
Background: Accurate methods for extraction of meaningful patterns in high dimensional data have become increasingly important with the recent generation of data types containing ...
Brandon W. Higgs, Jennifer W. Weller, Jeffrey L. S...
SDM
2011
SIAM
370views Data Mining» more  SDM 2011»
14 years 2 months ago
Sparse Latent Semantic Analysis
Latent semantic analysis (LSA), as one of the most popular unsupervised dimension reduction tools, has a wide range of applications in text mining and information retrieval. The k...
Xi Chen, Yanjun Qi, Bing Bai, Qihang Lin, Jaime G....
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ICCV
2007
IEEE
16 years 1 months ago
Shape Reconstruction Based on Similarity in Radiance Changes under Varying Illumination
This paper presents a technique for determining an object's shape based on the similarity of radiance changes observed at points on its surface under varying illumination. To...
Imari Sato, Takahiro Okabe, Qiong Yu, Yoichi Sato