In this paper, we introduce a Regression Nearest Neighbor framework for general classification tasks. To alleviate potential problems caused by nonlinearity, we propose a kernel regression nearest neighbor (KRNN) algorithm and its convex counterpart (CKRNN) as two specific extensions of nearest neighbor algorithm and present a fast and useful kernel selection method correspondingly. Comprehensive analysis and extensive experiments are used to demonstrate the effectiveness of our methods in real face datasets.Computer Science, Artificial IntelligenceCPCI-S(ISTP)
AbstractIn order to improve the reliability of facial expression recognition system, and reduce the ...
[[abstract]]The performance of a face recognition system degrades incredibly due to the variation of...
Nearest neighbor search is commonly employed in face recognition but it does not scale well to large...
Nearest subspace (NS) classification based on linear regression technique is a very straightforward ...
In this paper, we present novel ridge regression (RR) and kernel ridge regression (KRR) techniques f...
In this paper, we propose a new supervised linear feature extraction technique for multiclass classi...
Nearest subspace (NS) classification based on linear regression technique is a very straightforward ...
Fast and approximate nearest-neighbor search methods have recently become popular for scaling nonpar...
We propose a new collaborative neighbor representation algorithm for face recognition based on a rev...
In this article, we perform an extended analysis of different face-processing techniques for gen-der...
High feature dimensionality of realistic datasets adversely affects the recognition accuracy of near...
Nearest neighbor search is commonly employed in face recognition but it does not scale well to large...
In this paper we propose a novel kernel sparse representation classification (SRC) framework and uti...
This paper presents the results of a comparative study of linear and kernel-based methods for face r...
We propose a novel manifold learning approach, called Neighborhood Discriminant Projection (NDP), fo...
AbstractIn order to improve the reliability of facial expression recognition system, and reduce the ...
[[abstract]]The performance of a face recognition system degrades incredibly due to the variation of...
Nearest neighbor search is commonly employed in face recognition but it does not scale well to large...
Nearest subspace (NS) classification based on linear regression technique is a very straightforward ...
In this paper, we present novel ridge regression (RR) and kernel ridge regression (KRR) techniques f...
In this paper, we propose a new supervised linear feature extraction technique for multiclass classi...
Nearest subspace (NS) classification based on linear regression technique is a very straightforward ...
Fast and approximate nearest-neighbor search methods have recently become popular for scaling nonpar...
We propose a new collaborative neighbor representation algorithm for face recognition based on a rev...
In this article, we perform an extended analysis of different face-processing techniques for gen-der...
High feature dimensionality of realistic datasets adversely affects the recognition accuracy of near...
Nearest neighbor search is commonly employed in face recognition but it does not scale well to large...
In this paper we propose a novel kernel sparse representation classification (SRC) framework and uti...
This paper presents the results of a comparative study of linear and kernel-based methods for face r...
We propose a novel manifold learning approach, called Neighborhood Discriminant Projection (NDP), fo...
AbstractIn order to improve the reliability of facial expression recognition system, and reduce the ...
[[abstract]]The performance of a face recognition system degrades incredibly due to the variation of...
Nearest neighbor search is commonly employed in face recognition but it does not scale well to large...