There are basically two strategies which can be used to discriminate high dimensional spectral data. It is common practice to first reduce the dimensionality by some feature extraction preprocessing method, and then use an appropriate (low-dimensional) classifier. An alternative procedure is to use a (high-dimensional) classifier which is capable of handling a large number of variables. We introduce some novel dimension reducing techniques as well as low and high dimensional classifiers which have evolved only recently. The discrete wavelet transform is introduced as a method for extracting features. The Fourier transform, principal component analysis, stepwise strategies, and other variable selection methods for reducing the dimensionality...
Hyperspectral data contains rich spectral information and so have become very useful in data classif...
Abstract. We propose a new method of discriminant analysis, called High Dimensional Discriminant Ana...
International audienceWe propose a new method of discriminant analysis, called High Di- mensional Di...
There are basically two strategies which can be used to discriminate high dimensional spectral data....
This thesis is concerned with the application of statistical methods to spectral data. A\ud major co...
This thesis is concerned with the application of statistical methods to spectral data. A major conc...
A major concern arising from the classification of spectral data is that the number of variables or ...
A major concern arising from the classification of spectral data is that the number of variables or ...
Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant f...
Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant f...
Classification of very high dimensional images is of the almost interest in Remote Sensing applicati...
The recent development of more sophisticated spectroscopic methods allows acqui- sition of high dime...
The recent development of more sophisticated spectroscopic methods allows acquisition of high dimens...
Hyperspectral data contains rich spectral information and so have become very useful in data classif...
Abstract—Linear discriminant analysis (LDA) is a popular approach for dimensionality reduction for p...
Hyperspectral data contains rich spectral information and so have become very useful in data classif...
Abstract. We propose a new method of discriminant analysis, called High Dimensional Discriminant Ana...
International audienceWe propose a new method of discriminant analysis, called High Di- mensional Di...
There are basically two strategies which can be used to discriminate high dimensional spectral data....
This thesis is concerned with the application of statistical methods to spectral data. A\ud major co...
This thesis is concerned with the application of statistical methods to spectral data. A major conc...
A major concern arising from the classification of spectral data is that the number of variables or ...
A major concern arising from the classification of spectral data is that the number of variables or ...
Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant f...
Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant f...
Classification of very high dimensional images is of the almost interest in Remote Sensing applicati...
The recent development of more sophisticated spectroscopic methods allows acqui- sition of high dime...
The recent development of more sophisticated spectroscopic methods allows acquisition of high dimens...
Hyperspectral data contains rich spectral information and so have become very useful in data classif...
Abstract—Linear discriminant analysis (LDA) is a popular approach for dimensionality reduction for p...
Hyperspectral data contains rich spectral information and so have become very useful in data classif...
Abstract. We propose a new method of discriminant analysis, called High Dimensional Discriminant Ana...
International audienceWe propose a new method of discriminant analysis, called High Di- mensional Di...