Semi-supervised Discriminant Analysis

Cai, Deng; He, Xiaofei; Han, Jiawei

doi:10.1109/iccv.2007.4408856

Cited by 680 publications

(475 citation statements)

References 17 publications

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“…PCA-p and LDA-p, which denote PCA and LDA performing on the partial labeled data set respectively. In addition, we also compare our DPCA with SDA which is proposed in [12]. Figure 1 indicates that, in most cases, DPCA achieves the best performances among the 6 algorithms except LDA.…”

Section: Methodsmentioning

confidence: 99%

“…Lu et al [9] proposed a novel hybrid dimension reduction scheme to merge LDA and PCA in a unified framework. In addition, many subspace learning algorithms such as spectral regression discriminant analysis method [10,11] and semi-supervised discriminant analysis method [12] have been proposed. Specifically, Cai et al [12] proposed the semi-supervised discriminant analysis method called SDA which utilized local neighborhood information of labeled data for dimensionality reduction.…”

Section: Related Workmentioning

confidence: 99%

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A New Discriminant Principal Component Analysis Method with Partial Supervision

Sun

Zhang

2009

Neural Process Lett

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Principal component analysis (PCA) is one of the most widely used unsupervised dimensionality reduction methods in pattern recognition. It preserves the global covariance structure of data when labels of data are not available. However, in many practical applications, besides the large amount of unlabeled data, it is also possible to obtain partial supervision such as a few labeled data and pairwise constraints, which contain much more valuable information for discrimination than unlabeled data. Unfortunately, PCA cannot utilize that useful discriminant information effectively. On the other hand, traditional supervised dimensionality reduction methods such as linear discriminant analysis perform on only labeled data. When labeled data are insufficient, their performances will deteriorate. In this paper, we propose a novel discriminant PCA (DPCA) model to boost the discriminant power of PCA when both unlabeled and labeled data as well as pairwise constraints are available. The derived DPCA algorithm is efficient and has a closed form solution. Experimental results on several UCI and face data sets show that DPCA is superior to several established dimensionality reduction methods.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A New Discriminant Principal Component Analysis Method with Partial Supervision

Sun

Zhang

2009

Neural Process Lett

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“…In this subsection, we compare our SNDA algorithm with five popular algorithms in dimensionality reduction: PCA−LDA [8], PCA−LFDA [15], NDA [10], LPP [7] and SDA [11]. After dimensionality reduction has been performed, we apply a simple nearest-neighbor classifier to perform classification in the embedding space.…”

Section: Real World Datamentioning

confidence: 99%

“…Semi-supervised Discriminant Analysis (SDA) [11] which makes use of both labeled and unlabeled samples is a reasonable solution to deal with the problem of insufficient training (labeled) samples. However, like other LDA-based methods, SDA still assumes the samples in each class satisfy the Gaussian distribution.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Nonparametric Discriminant Analysis

Xing

Jiang

2013

IEICE Trans. Inf. & Syst.

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SUMMARYWe extend the Nonparametric Discriminant Analysis (NDA) algorithm to a semi-supervised dimensionality reduction technique, called Semi-supervised Nonparametric Discriminant Analysis (SNDA). SNDA preserves the inherent advantages of NDA, that is, relaxing the Gaussian assumption required for the traditional LDA-based methods. SNDA takes advantage of both the discriminating power provided by the NDA method and the locality-preserving power provided by the manifold learning. Specifically, the labeled data points are used to maximize the separability between different classes and both the labeled and unlabeled data points are used to build a graph incorporating neighborhood information of the data set. Experiments on synthetic as well as real datasets demonstrate the effectiveness of the proposed approach. key words: semi-supervised learning, nonparametric discriminant analysis, manifold learning

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“…We propose a novel semi-supervised discriminant analysis algorithm called SSDA CCCP . Although there already exists another semi-supervised LDA algorithm, called SDA [23], which exploits the local neighborhood information of data points in performing dimensionality reduction, our SSDA CCCP algorithm works in a very different way. Specifically, we utilize unlabeled data to maximize an optimality criterion of LDA and formulate the problem as a constrained optimization problem that can be solved using the constrained concave-convex procedure (CCCP) [24,25].…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised Discriminant Analysis Via CCCP

Zhang

Yeung

Machine Learning and Knowledge Discovery in Databases

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Abstract. 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 algorithm called SSDACCCP . We utilize unlabeled data to maximize an optimality criterion of LDA and use the constrained concave-convex procedure to solve the optimization problem. The optimization procedure leads to estimation of the class labels for the unlabeled data. We propose a novel confidence measure for selecting those unlabeled data points with high confidence. The selected unlabeled data can then be used to augment the original labeled data set for performing LDA. We also propose a variant of SSDACCCP , called M-SSDACCCP , which adopts the manifold assumption to utilize the unlabeled data. Extensive experiments on many benchmark data sets demonstrate the effectiveness of our proposed methods.

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Semi-supervised Discriminant Analysis

Abstract: Linear Discriminant Analysis (LDA)

Cited by 680 publications

References 17 publications

A New Discriminant Principal Component Analysis Method with Partial Supervision

A New Discriminant Principal Component Analysis Method with Partial Supervision

Semi-Supervised Nonparametric Discriminant Analysis

Semi-supervised Discriminant Analysis Via CCCP

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