Sparse Representation for Classification of Tumors Using Gene Expression Data

Hang, Xiyi; Wu, Fang‐Xiang

doi:10.1155/2009/403689

Cited by 67 publications

(75 citation statements)

References 27 publications

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“…(10) can also be determined using this method. Note that, the results of SVM and SRC in our experiments are slightly different from those reported in [19,29]. This is probably because the distribution file of cross validation in our experiments is different from those in [19,29].…”

Section: Two-class Classificationcontrasting

confidence: 75%

“…The proposed method is evaluated in comparison with some representative methods, including the SRC [18,19], LASSO [40] and the widely used support vector machines (SVM) [28]. SVM has been successfully used for gene profile classification [28].…”

Section: Evaluation Of Performancementioning

confidence: 99%

“…Unlike conventional supervised learning methods, where a training procedure is used to create a classification model for testing, the SRC does not contain separate training and testing stages so that the over-fitting problem is much lessened. The SRC method has been successfully used in face recognition [18] and tumor classification [19]. In these methods, a testing sample is represented as the linear combination of the original training samples, and the representation error over each class is used as an indicator to classify the testing sample.…”

Section: Introductionmentioning

confidence: 99%

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Metasample-Based Sparse Representation for Tumor Classification

Zheng

Zhang

et al. 2011

IEEE/ACM Trans. Comput. Biol. and Bioinf.

110

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A reliable and accurate identification of the type of tumors is crucial to the proper treatment of cancers. In recent years, it has been shown that sparse representation (SR) by 1 l -norm minimization is robust to noise, outliers and even incomplete measurements, and SR has been successfully used for classification. This paper presents a new SR based method for tumor classification using gene expression data. A set of metasamples are extracted from the training samples, and then an input testing sample is represented as the linear combination of these metasamples by l 1 -regularized least square method. Classification is achieved by using a discriminating function defined on the representation coefficients. Since l 1 -norm minimization leads to a sparse solution, the proposed method is called metasample based SR classification (MSRC).Extensive experiments on publicly available gene expression datasets show that MSRC is efficient for tumor classification, achieving higher accuracy than many existing representative schemes.

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Section: Two-class Classificationcontrasting

confidence: 75%

Section: Evaluation Of Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Metasample-Based Sparse Representation for Tumor Classification

Zheng

Zhang

et al. 2011

IEEE/ACM Trans. Comput. Biol. and Bioinf.

110

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“…This algorithm exploits the discriminative nature of sparse representation and the reconstruction of the test sample provides directly its classification label. This idea naturally extends to other signal classification problems such as iris recognition [18], tumor classification [19], and HSI unmixing [20].…”

Section: Introductionmentioning

confidence: 93%

Sparse Representation for Target Detection in Hyperspectral Imagery

Chen

Nasrabadi

Tran

2011

IEEE J. Sel. Top. Signal Process.

381

186

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Abstract-In this paper, we propose a new sparsity-based algorithm for automatic target detection in hyperspectral imagery (HSI). This algorithm is based on the concept that a pixel in HSI lies in a low-dimensional subspace and thus can be represented as a sparse linear combination of the training samples. The sparse representation (a sparse vector corresponding to the linear combination of a few selected training samples) of a test sample can be recovered by solving an 0 -norm minimization problem. With the recent development of the compressed sensing theory, such minimization problem can be recast as a standard linear programming problem or efficiently approximated by greedy pursuit algorithms. Once the sparse vector is obtained, the class of the test sample can be determined by the characteristics of the sparse vector on reconstruction. In addition to the constraints on sparsity and reconstruction accuracy, we also exploit the fact that in HSI the neighboring pixels have a similar spectral characteristic (smoothness). In our proposed algorithm, a smoothness constraint is also imposed by forcing the vector Laplacian at each reconstructed pixel to be minimum all the time within the minimization process. The proposed sparsity-based algorithm is applied to several hyperspectral imagery to detect targets of interest. Simulation results show that our algorithm outperforms the classical hyperspectral target detection algorithms, such as the popular spectral matched filters, matched subspace detectors, adaptive subspace detectors, as well as binary classifiers such as support vector machines.

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“…Sparse representations have been recently exploited in many pattern recognition applications [1][2][3]. These approaches are based on the assumption that a test sample approximately lies in a lowdimensional subspace spanned by the training data and thus can be compactly represented by a few training samples.…”

Section: Introductionmentioning

confidence: 99%

Robust face recognition using locally adaptive sparse representation

Chen

Thong

Tran

2010

2010 IEEE International Conference on Image Processing

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This paper presents a block-based face-recognition algorithm based on a sparse linear-regression subspace model via locally adaptive dictionary constructed from past observable data (training samples). The local features of the algorithm provide an immediate benefit -the increase in robustness level to various registration errors. Our proposed approach is inspired by the way human beings often compare faces when presented with a tough decision: we analyze a series of local discriminative features (do the eyes match? how about the nose? what about the chin?...) and then make the final classification decision based on the fusion of local recognition results. In other words, our algorithm attempts to represent a block in an incoming test image as a linear combination of only a few atoms in a dictionary consisting of neighboring blocks in the same region across all training samples. The results of a series of these sparse local representations are used directly for recognition via either maximum likelihood fusion or a simple democratic majority voting scheme. Simulation results on standard face databases demonstrate the effectiveness of the proposed algorithm in the presence of multiple mis-registration errors such as translation, rotation, and scaling.

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Sparse Representation for Classification of Tumors Using Gene Expression Data

Cited by 67 publications

References 27 publications

Metasample-Based Sparse Representation for Tumor Classification

Metasample-Based Sparse Representation for Tumor Classification

Sparse Representation for Target Detection in Hyperspectral Imagery

Robust face recognition using locally adaptive sparse representation

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