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Semi-supervised discriminant analysis

by Deng Cai, Xiaofei He, Jiawei Han - in Proc. of the IEEE Int’l Conf. on Comp. Vision (ICCV), Rio De Janeiro , 2007
"... Linear Discriminant Analysis (LDA) has been a popular method for extracting features which preserve class separability. The projection vectors are commonly obtained by maximizing the between class covariance and simultaneously minimizing the within class covariance. In practice, when there is no suf ..."
Abstract - Cited by 102 (2 self) - Add to MetaCart
there is no sufficient training samples, the covariance matrix of each class may not be accurately estimated. In this paper, we propose a novel method, called Semisupervised Discriminant Analysis (SDA), which makes use of both labeled and unlabeled samples. The labeled data points are used to maximize the separability

Semi-Supervised Discriminant Analysis via CCCP

by Yu Zhang, Dit-yan Yeung
"... Linear discriminant analysis (LDA) is commonly used for dimensionality reduction. In real-world applications where labeled data are scarce, LDA does not work very well. However, unlabeled data are often available in large quantities. We propose a novel semi-supervised discriminant analysis algorith ..."
Abstract - Cited by 9 (2 self) - Add to MetaCart
Linear discriminant analysis (LDA) is commonly used for dimensionality reduction. In real-world applications where labeled data are scarce, LDA does not work very well. However, unlabeled data are often available in large quantities. We propose a novel semi-supervised discriminant analysis

Semi-supervised Discriminant Analysis Based on UDP Regularization

by Huining Qiu, Jianhuang Lai, Jian Huang, Yu Chen
"... We propose a semi-supervised learning algorithm for discriminant analysis, which uses the geometric structure of both labeled and unlabeled samples and perform a manifold regularization on LDA. The labeled data points provide labeling information and the unlabeled data points provide extra geometric ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
We propose a semi-supervised learning algorithm for discriminant analysis, which uses the geometric structure of both labeled and unlabeled samples and perform a manifold regularization on LDA. The labeled data points provide labeling information and the unlabeled data points provide extra

T.: Beyond the graphs: Semi-parametric semi-supervised discriminant analysis

by Fei Wang, Xin Wang, Tao Li - In: CVPR , 2009
"... Linear Discriminant Analysis (LDA) is a popular feature extraction method that has aroused considerable interests in computer vision and pattern recognition fields. The projection vectors of LDA is usually achieved by maximizing the between-class scatter and simultaneously minimizing the within-clas ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
-class scatter of the data set. However, in practice, there is usually a lack of sufficient labeled data, which makes the estimated projection direction inaccurate. To address the above limitations, in this paper, we propose a novel semi-supervised discriminant analysis approach. Unlike traditional graph based

Semi-Supervised Discriminant Analysis Using Robust Path-Based Similarity

by Yu Zhang, Dit-yan Yeung - Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2008
"... Linear Discriminant Analysis (LDA), which works by maximizing the within-class similarity and minimizing the between-class similarity simultaneously, is a popular dimensionality reduction technique in pattern recognition and machine learning. In real-world applications when labeled data are limited, ..."
Abstract - Cited by 13 (2 self) - Add to MetaCart
, LDA does not work well. Under many situations, however, it is easy to obtain unlabeled data in large quantities. In this paper, we propose a novel dimensionality reduction method, called Semi-Supervised Discriminant Analysis (SSDA), which can utilize both labeled and unlabeled data to perform

Linear discriminant analysis Semi-supervised discriminant analysis Sparse representation

by Graph Construction , 2009
"... reco based semi-supervised dimensionality reduction (SSDR) provides a feasible strategy to deal with such and localizing the training image (Chen et al., 2004), are generally required to deal with the single training sample problem. One can refer to a recent survey (Tan et al., 2006) for more detail ..."
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reco based semi-supervised dimensionality reduction (SSDR) provides a feasible strategy to deal with such and localizing the training image (Chen et al., 2004), are generally required to deal with the single training sample problem. One can refer to a recent survey (Tan et al., 2006) for more

A new Graph constructor for Semi-supervised Discriminant Analysis via Group Sparsity

by Haoyuan Gao, Liansheng Zhuang, Nenghai Yu
"... Abstract—Semi-supervised dimensionality reduction is very important in mining high-dimensional data due to the lack of costly labeled data. This paper studies the Semi-supervised Dis-criminant Analysis (SDA) algorithm, which aims at dimension-ality reduction utilizing both limited labeled data and a ..."
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Abstract—Semi-supervised dimensionality reduction is very important in mining high-dimensional data due to the lack of costly labeled data. This paper studies the Semi-supervised Dis-criminant Analysis (SDA) algorithm, which aims at dimension-ality reduction utilizing both limited labeled data

Semi-supervised bilinear subspace learning

by Dong Xu, Shuicheng Yan - IEEE Trans. Image Process , 2009
"... Abstract—Recent research has demonstrated the success of tensor based subspace learning in both unsupervised and supervised configurations (e.g., 2-D PCA, 2-D LDA, and DATER). In this correspondence, we present a new semi-supervised subspace learning algorithm by integrating the tensor representatio ..."
Abstract - Cited by 14 (3 self) - Add to MetaCart
. An iterative algorithm, referred to as adaptive reg-ularization based semi-supervised discriminant analysis with tensor rep-resentation (ARSDA/T), is also developed to compute the solution. In ad-dition to handling tensor data, a vector-based variant (ARSDA/V) is also presented, in which the tensor data

Brief Papers Semi-Supervised Dimension Reduction Using Trace Ratio Criterion

by Yi Huang, Dong Xu, Feiping Nie
"... Abstract — In this brief, we address the trace ratio (TR) problem for semi-supervised dimension reduction. We first refor-mulate the objective function of the recent work semi-supervised discriminant analysis (SDA) in a TR form. We also observe that in SDA the low-dimensional data representation F i ..."
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Abstract — In this brief, we address the trace ratio (TR) problem for semi-supervised dimension reduction. We first refor-mulate the objective function of the recent work semi-supervised discriminant analysis (SDA) in a TR form. We also observe that in SDA the low-dimensional data representation F

Semi-supervised discriminant hashing

by Saehoon Kim, Seungjin Choi - in Proceedings of the IEEE International Conference on Data Mining (ICDM , 2011
"... Abstract—Hashing refers to methods for embedding highdimensional data into a similarity-preserving low-dimensional Hamming space such that similar objects are indexed by binary codes whose Hamming distances are small. Learning hash functions from data has recently been recognized as a promising appr ..."
Abstract - Cited by 5 (3 self) - Add to MetaCart
and cannot-link) using labeled data are leveraged while unlabeled data are used for regularization to avoid over-fitting. In this paper we base our semi-supervised hashing on linear discriminant analysis, where hash functions are learned such that labeled data are used to maximize the separability between
Results 1 - 10 of 16,111