Sciweavers

Share
IBPRIA
2003
Springer
10 years 3 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
AIPR
2003
IEEE
10 years 3 months ago
Band Selection Using Independent Component Analysis for Hyperspectral Image Processing
Although hyperspectral images provide abundant information about objects, their high dimensionality also substantially increases computational burden. Dimensionality reduction off...
Hongtao Du, Hairong Qi, Xiaoling Wang, Rajeev Rama...
IDEAL
2004
Springer
10 years 4 months ago
Dimensionality Reduction with Image Data
A common objective in image analysis is dimensionality reduction. The most common often used data-exploratory technique with this objective is principal component analysis. We pro...
Mónica Benito, Daniel Peña
SLSFS
2005
Springer
10 years 4 months ago
Auxiliary Variational Information Maximization for Dimensionality Reduction
Abstract. Mutual Information (MI) is a long studied measure of information content, and many attempts to apply it to feature extraction and stochastic coding have been made. Howeve...
Felix V. Agakov, David Barber
ECML
2005
Springer
10 years 4 months ago
Fast Non-negative Dimensionality Reduction for Protein Fold Recognition
Abstract. In this paper, dimensionality reduction via matrix factorization with nonnegativity constraints is studied. Because of these constraints, it stands apart from other linea...
Oleg Okun, Helen Priisalu, Alexessander Alves
SIGIR
2005
ACM
10 years 4 months ago
Multi-label informed latent semantic indexing
Latent semantic indexing (LSI) is a well-known unsupervised approach for dimensionality reduction in information retrieval. However if the output information (i.e. category labels...
Kai Yu, Shipeng Yu, Volker Tresp
SAC
2005
ACM
10 years 4 months ago
Estimating manifold dimension by inversion error
Video and image datasets can often be described by a small number of parameters, even though each image usually consists of hundreds or thousands of pixels. This observation is of...
Shawn Martin, Alex Bäcker
BIOWIRE
2007
Springer
10 years 4 months ago
Beta Random Projection
Random projection (RP) is a common technique for dimensionality reduction under L2 norm for which many significant space embedding results have been demonstrated. In particular, r...
Yu-En Lu, Pietro Liò, Steven Hand
AUSDM
2007
Springer
107views Data Mining» more  AUSDM 2007»
10 years 4 months ago
A Discriminant Analysis for Undersampled Data
One of the inherent problems in pattern recognition is the undersampled data problem, also known as the curse of dimensionality reduction. In this paper a new algorithm called pai...
Matthew Robards, Junbin Gao, Philip Charlton
ROMAN
2007
IEEE
191views Robotics» more  ROMAN 2007»
10 years 4 months ago
Learning and Recognition of Object Manipulation Actions Using Linear and Nonlinear Dimensionality Reduction
— In this work, we perform an extensive statistical evaluation for learning and recognition of object manipulation actions. We concentrate on single arm/hand actions but study th...
Isabel Serrano Vicente, Danica Kragic, Jan-Olof Ek...
books