Topics
orthogonalAgent
dimension reductionElection
Principal Component Analysis (PCA)Anatomical structure
algorithmMusic genre
In undersampled problems where the number of samples is smaller than the di-mension of data space, it is difficult to apply Linear Discriminant Analysis (LDA) due to the singularity of scatter matrices caused by high dimensionality. We propose a fast dimension reduction method based on a simple modification of Principal Component Analysis (PCA) and the orthogonal decomposition. The proposed algorithm is an ef-ficient way to perform LDA for undersampled problems. Our experimental results in face recognition and text classification demonstrate the effectiveness of our proposed method
Face recognition has recently received significant attention as one of the challenging and promising...
With rapid development of image recognition technology and increasing demand for a fast yet robust c...
In order to accelerate data processing and improve classification accuracy, some classic dimension r...
In undersampled problems where the number of samples is smaller than the dimension of data space, it...
We proposed a face recognition algorithm based on both the multilinear principal component analysis ...
In this paper, we conduct a comprehensive study on dimensionality reduction (DR) techniques and disc...
Abstract – In face recognition, LDA often encounters the so-called small sample size (SSS) problem, ...
This paper presents a novel dimensionality reduction algorithm for kernel based classification. In t...
Linear Discriminant Analysis (LDA) has been successfully used as a dimensionality reduction techniqu
Abstract Recently, many dimensionality reduction algorithms, including local methods and global meth...
The aim of this paper is to present a comparative study of two linear dimension reduction methods na...
Abstract—This paper addresses the dimension reduction problem in Fisherface for face recognition. Wh...
Information explosion has occurred in most of the sciences and researches due to advances in data co...
A novel tensor based method is prepared to solve the supervised dimensionality reduction problem. In...
Principal Component Analysis (PCA) is an example of a successful unsupervised statistical dimensiona...
Face recognition has recently received significant attention as one of the challenging and promising...
With rapid development of image recognition technology and increasing demand for a fast yet robust c...
In order to accelerate data processing and improve classification accuracy, some classic dimension r...
In undersampled problems where the number of samples is smaller than the dimension of data space, it...
We proposed a face recognition algorithm based on both the multilinear principal component analysis ...
In this paper, we conduct a comprehensive study on dimensionality reduction (DR) techniques and disc...
Abstract – In face recognition, LDA often encounters the so-called small sample size (SSS) problem, ...
This paper presents a novel dimensionality reduction algorithm for kernel based classification. In t...
Linear Discriminant Analysis (LDA) has been successfully used as a dimensionality reduction techniqu
Abstract Recently, many dimensionality reduction algorithms, including local methods and global meth...
The aim of this paper is to present a comparative study of two linear dimension reduction methods na...
Abstract—This paper addresses the dimension reduction problem in Fisherface for face recognition. Wh...
Information explosion has occurred in most of the sciences and researches due to advances in data co...
A novel tensor based method is prepared to solve the supervised dimensionality reduction problem. In...
Principal Component Analysis (PCA) is an example of a successful unsupervised statistical dimensiona...
Face recognition has recently received significant attention as one of the challenging and promising...
With rapid development of image recognition technology and increasing demand for a fast yet robust c...
In order to accelerate data processing and improve classification accuracy, some classic dimension r...
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