Add to ORKGAbstract: In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for image feature extraction and pattern recognition based on graph embedded learning and under the Fisher discirminant analysis framework. In MMDA, the within-class graph and between-class graph are respectively designed to characterize the within-class compactness and the between-class separability, seeking for the discriminant matrix to simultaneously maximize the between-class scatter and minimize the within-class scatter. In addition, in MMDA, the within-class graph can represent the sub-manifold information, while the between-class graph can represent the multi-manifold information. The proposed MMDA is extensively examined by using the FERET, AR ...
2011 18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, 11-14 September 20...
Traditional manifold learning methods generally include a single projection stage that maps high-dim...
Abstract—Over the past few decades, a large family of algorithms—supervised or unsupervised; stemmin...
In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for an image feature ...
Previous manifold learning algorithms mainly focus on uncovering the low dimensional geometry struct...
Abstract—Manifold learning is an important feature extrac-tion approach in data mining. This paper p...
Abstract. In this paper we propose a novel non-linear discriminative analysis technique for manifold...
Abstract—Conventional appearance-based face recognition methods usually assume that there are multip...
Conventional appearance-based face recognition meth-ods usually assume there are multiple samples pe...
A convenient way of dealing with image sets is to represent them as points on Grassmannian manifolds...
Usually many real datasets in pattern recognition applications contain a large quantity of noisy and...
which permits unrestricted use, distribution, and reproduction in any medium, provided the original ...
Manifold learning aims to map the original data from a high-dimensional space into a low-dimensional...
In many image classification applications, it is common to extract multiple visual features from dif...
We propose a novel manifold learning approach, called Neighborhood Discriminant Projection (NDP), fo...
2011 18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, 11-14 September 20...
Traditional manifold learning methods generally include a single projection stage that maps high-dim...
Abstract—Over the past few decades, a large family of algorithms—supervised or unsupervised; stemmin...
In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for an image feature ...
Previous manifold learning algorithms mainly focus on uncovering the low dimensional geometry struct...
Abstract—Manifold learning is an important feature extrac-tion approach in data mining. This paper p...
Abstract. In this paper we propose a novel non-linear discriminative analysis technique for manifold...
Abstract—Conventional appearance-based face recognition methods usually assume that there are multip...
Conventional appearance-based face recognition meth-ods usually assume there are multiple samples pe...
A convenient way of dealing with image sets is to represent them as points on Grassmannian manifolds...
Usually many real datasets in pattern recognition applications contain a large quantity of noisy and...
which permits unrestricted use, distribution, and reproduction in any medium, provided the original ...
Manifold learning aims to map the original data from a high-dimensional space into a low-dimensional...
In many image classification applications, it is common to extract multiple visual features from dif...
We propose a novel manifold learning approach, called Neighborhood Discriminant Projection (NDP), fo...
2011 18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, 11-14 September 20...
Traditional manifold learning methods generally include a single projection stage that maps high-dim...
Abstract—Over the past few decades, a large family of algorithms—supervised or unsupervised; stemmin...