127 search results - page 1 / 26 » Binary data comparison using similarity indices and principa... |
IGARSS
2009
13 years 2 months ago
2009
Kernel Principal Component Analysis (KPCA) is investigated for feature extraction from hyperspectral remotesensing data. Features extracted using KPCA are used to construct the Ex...
ICONIP
2007
13 years 6 months ago
2007
Abstract. Principal component analysis (PCA) is a widely used technique for data analysis and dimensionality reduction. Eigenvalue decomposition is the standard algorithm for solvi...
COMPLEX
2009
Springer
13 years 11 months ago
2009
Springer
To probe network characteristics, two predominant ways of network comparison are global property statistics and subgraph enumeration. However, they suffer from limited information...
ICML
2004
IEEE
14 years 5 months ago
2004
IEEE
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
ECCV
2010
Springer
13 years 9 months ago
2010
Springer
Manifolds are widely used to model non-linearity arising in a range of computer vision applications. This paper treats statistics on manifolds and the loss of accuracy occurring wh...