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IGARSS
2009
9 years 8 months ago
Classification Performance of Random-projection-based Dimensionality Reduction of Hyperspectral Imagery
High-dimensional data such as hyperspectral imagery is traditionally acquired in full dimensionality before being reduced in dimension prior to processing. Conventional dimensiona...
James E. Fowler, Qian Du, Wei Zhu, Nicolas H. Youn...
WEBI
2010
Springer
9 years 8 months ago
DSP: Robust Semi-supervised Dimensionality Reduction Using Dual Subspace Projections
High-dimensional data usually incur learning deficiencies and computational difficulties. We present a novel semi-supervised dimensionality reduction technique that embeds high-dim...
Su Yan, Sofien Bouaziz, Dongwon Lee
PKDD
2010
Springer
179views Data Mining» more  PKDD 2010»
9 years 8 months ago
Learning an Affine Transformation for Non-linear Dimensionality Reduction
The foremost nonlinear dimensionality reduction algorithms provide an embedding only for the given training data, with no straightforward extension for test points. This shortcomin...
Pooyan Khajehpour Tadavani, Ali Ghodsi
PAKDD
2010
ACM
173views Data Mining» more  PAKDD 2010»
9 years 8 months ago
Distributed Knowledge Discovery with Non Linear Dimensionality Reduction
Data mining tasks results are usually improved by reducing the dimensionality of data. This improvement however is achieved harder in the case that data lay on a non linear manifol...
Panagis Magdalinos, Michalis Vazirgiannis, Dialect...
ISDA
2010
IEEE
9 years 8 months ago
Feature selection is the ReliefF for multiple instance learning
Dimensionality reduction and feature selection in particular are known to be of a great help for making supervised learning more effective and efficient. Many different feature sel...
Amelia Zafra, Mykola Pechenizkiy, Sebastián...
BMVC
2010
9 years 8 months ago
Iterative Hyperplane Merging: A Framework for Manifold Learning
We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be...
Harry Strange, Reyer Zwiggelaar
PRL
2010
188views more  PRL 2010»
9 years 9 months ago
Sparsity preserving discriminant analysis for single training image face recognition
: Single training image face recognition is one of main challenges to appearance-based pattern recognition techniques. Many classical dimensionality reduction methods such as LDA h...
Lishan Qiao, Songcan Chen, Xiaoyang Tan
PR
2010
170views more  PR 2010»
9 years 9 months ago
Sparsity preserving projections with applications to face recognition
: Dimensionality reduction methods (DRs) have commonly been used as a principled way to understand the high-dimensional data such as face images. In this paper, we propose a new un...
Lishan Qiao, Songcan Chen, Xiaoyang Tan
NN
2010
Springer
183views Neural Networks» more  NN 2010»
9 years 9 months ago
Dimensionality reduction for density ratio estimation in high-dimensional spaces
The ratio of two probability density functions is becoming a quantity of interest these days in the machine learning and data mining communities since it can be used for various d...
Masashi Sugiyama, Motoaki Kawanabe, Pui Ling Chui
CORR
2010
Springer
92views Education» more  CORR 2010»
9 years 9 months ago
Random Projections for $k$-means Clustering
This paper discusses the topic of dimensionality reduction for k-means clustering. We prove that any set of n points in d dimensions (rows in a matrix A ∈ Rn×d ) can be project...
Christos Boutsidis, Anastasios Zouzias, Petros Dri...
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