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» On the Optimality of the Dimensionality Reduction Method
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CVPR
2008
IEEE
16 years 8 months ago
A unified framework for generalized Linear Discriminant Analysis
Linear Discriminant Analysis (LDA) is one of the wellknown methods for supervised dimensionality reduction. Over the years, many LDA-based algorithms have been developed to cope w...
Shuiwang Ji, Jieping Ye
IBPRIA
2003
Springer
15 years 11 months ago
Supervised Locally Linear Embedding Algorithm for Pattern Recognition
The dimensionality of the input data often far exceeds their intrinsic dimensionality. As a result, it may be difficult to recognize multidimensional data, especially if the number...
Olga Kouropteva, Oleg Okun, Matti Pietikäinen
CIKM
2008
Springer
15 years 8 months ago
On low dimensional random projections and similarity search
Random projection (RP) is a common technique for dimensionality reduction under L2 norm for which many significant space embedding results have been demonstrated. However, many si...
Yu-En Lu, Pietro Liò, Steven Hand
ICML
2004
IEEE
16 years 7 months ago
Generative modeling for continuous non-linearly embedded visual inference
Many difficult visual perception problems, like 3D human motion estimation, can be formulated in terms of inference using complex generative models, defined over high-dimensional ...
Cristian Sminchisescu, Allan D. Jepson
CVPR
2004
IEEE
15 years 10 months ago
Visual Object Categorization Using Distance-Based Discriminant Analysis
This paper formulates the problem of object categorization in the discriminant analysis framework focusing on transforming visual feature data so as to make it conform to the comp...
Serhiy Kosinov, Stéphane Marchand-Maillet, ...