Add to ORKGIn microarray experiments, the dimension p of the data is very large but there are only a few observations N on the subjects/patients. In this article, the problem of classifying a subject into one of two groups, when p is large, is considered. Three procedures based on the Moore-Penrose inverse of the sample covariance matrix, and an empirical Bayes estimate of the precision matrix are proposed and compared with the DLDA procedure. Key words and phrases: Classification, discrimination analysis, minimum distance, Moore-Penrose inverse. 1
International audienceIn this paper, we deal with the issue of classifying normally distributed data...
Abstract. Dimensionality reduction can often improve the performance of the k-nearest neighbor class...
The purpose of the paper is to propose a new method for classification. Our MSPLS method was deduced...
High-dimensional data analysis characterises many contemporary problems in statistics and arise in m...
Advances in microarray technology have equipped researchers to measure gene expression levels simult...
Linear discriminant analysis (LDA) is one of the most popular methods of classification. For high-di...
This is the first book to compare eight LDFs by different types of datasets, such as Fisher’s iris d...
© 2016 Dr. Sabrina de Andrade Rodrigues"It has been claimed and demonstrated that many of the conclu...
Background: Data generated using ‘omics’ technologies are characterized by high dimensionality, wher...
Distance functions are a fundamental ingredient of classification and clustering procedures, and thi...
Abstract The recent technology development in the concern of microarray experiments has provided man...
This book shows how to decompose high-dimensional microarrays into small subspaces (Small Matryoshka...
A class of linear classification rules, specifically designed for high-dimensional problems, is prop...
Microarrays are applications of electrical engineering and technology in biology that allow simultan...
Dimensionality reduction can often improve the performance of the k-nearest neighbor classifier (kNN...
International audienceIn this paper, we deal with the issue of classifying normally distributed data...
Abstract. Dimensionality reduction can often improve the performance of the k-nearest neighbor class...
The purpose of the paper is to propose a new method for classification. Our MSPLS method was deduced...
High-dimensional data analysis characterises many contemporary problems in statistics and arise in m...
Advances in microarray technology have equipped researchers to measure gene expression levels simult...
Linear discriminant analysis (LDA) is one of the most popular methods of classification. For high-di...
This is the first book to compare eight LDFs by different types of datasets, such as Fisher’s iris d...
© 2016 Dr. Sabrina de Andrade Rodrigues"It has been claimed and demonstrated that many of the conclu...
Background: Data generated using ‘omics’ technologies are characterized by high dimensionality, wher...
Distance functions are a fundamental ingredient of classification and clustering procedures, and thi...
Abstract The recent technology development in the concern of microarray experiments has provided man...
This book shows how to decompose high-dimensional microarrays into small subspaces (Small Matryoshka...
A class of linear classification rules, specifically designed for high-dimensional problems, is prop...
Microarrays are applications of electrical engineering and technology in biology that allow simultan...
Dimensionality reduction can often improve the performance of the k-nearest neighbor classifier (kNN...
International audienceIn this paper, we deal with the issue of classifying normally distributed data...
Abstract. Dimensionality reduction can often improve the performance of the k-nearest neighbor class...
The purpose of the paper is to propose a new method for classification. Our MSPLS method was deduced...