Classification of gene-expression data: The manifold-based metric learning way

Lee, Jianguo; Zhang, Changshui

doi:10.1016/j.patcog.2006.05.026

Cited by 21 publications

(16 citation statements)

References 22 publications

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“…Such ''crossing'' is detrimental to the primary class separability. As a consequence, it causes NFL's performance degradation (Du and Chen 2007;Lee and Zhang 2006).…”

Section: Shortcomings Of Nflmentioning

confidence: 99%

“…Nevertheless, NFL's performance is challenged by the heavy FLspace ''intersecting'' (Lee and Zhang 2006;Zhao et al 2001;Zhou et al 2004). Neither LFL nor CFL can completely avoid such ''intersecting'' at all, as they both adopt FLs with the whole length.…”

Section: Generalization Abilitymentioning

confidence: 99%

“…Besides this, due to the infinite length of a line, the extension in NFL is likely to cause training subspaces ''intersecting'' (Lee and Zhang 2006;Zhao et al 2001;Zhou et al 2004). As a result, it destroys the original separation of different classes and degrades NFL's performance.…”

Section: Introductionmentioning

confidence: 99%

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Pattern classification based on k locally constrained line

Qing

Fang

2010

Soft Comput

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A simple yet effective learning algorithm, k locally constrained line (k-LCL), is presented for pattern classification. In k-LCL, any two prototypes of the same class are extended to a constrained line (CL), through which the representational capacity of the training set is largely improved. Because each CL is adjustable in length, k-LCL can well avoid the ''intersecting'' of training subspaces in most traditional feature classifiers. Moreover, to speed up the calculation, k-LCL classifies an unknown sample focusing only on its local CLs in each class. Experimental results, obtained on both synthetic and realworld benchmark data sets, show that the proposed method has better accuracy and efficiency than most existing feature line methods.

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“…Such ''crossing'' is detrimental to the primary class separability. As a consequence, it causes NFL's performance degradation (Du and Chen 2007;Lee and Zhang 2006).…”

Section: Shortcomings Of Nflmentioning

confidence: 99%

Section: Generalization Abilitymentioning

confidence: 99%

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Pattern classification based on k locally constrained line

Qing

Fang

2010

Soft Comput

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“…In order to improve the classification accuracy, many approaches have been proposed from the perspective of machine learning and pattern recognition Guyon et al, 2002;Lee and Zhang, 2006;Nevins and Potti, 2007;Liu and Huang, 2008;Schweikert et al, 2009;Zheng et al, 2009;Cai et al, 2010;Leung and Hung, 2010;Zare et al, 2011;Zheng et al, 2011;Wang et al, 2012). Despite of the success achieved by these advanced techniques, the improvement for the classification accuracy remains limited, because they only deal with the data obtained from the biological experiments, which contains noise and missing values.…”

Section: Introductionmentioning

confidence: 99%

Gene function prediction with knowledge from gene ontology

Shen

Zhang

2015

IJDMB

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Gene function prediction is an important problem in bioinformatics. Due to the inherent noise existing in the gene expression data, the attempt to improve the prediction accuracy resorting to new classification techniques is limited. With the emergence of Gene Ontology (GO), extra knowledge about the gene products can be extracted from GO and facilitates solving the gene function prediction problem. In this paper, we propose a new method which utilises GO information to improve the classifiers' performance in gene function prediction. Specifically, our method learns a distance metric under the supervision of the GO knowledge using the distance learning technique. Compared with the traditional distance metrics, the learned one produces a better performance and consequently classification accuracy can be improved. The effectiveness of our proposed method has been corroborated by the extensive experimental results.

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“…It aims to generate the low-dimensional representation of the high-dimensional data distributed on a smooth manifold with intrinsic low dimension. The case embodies many important applications in machine learning and pattern recognition [2] , image processing [3] , remote sensing [4] , biological data mining [5] and facial expression recognition [6] . So far a variety of manifold learning methods have been developed, such as locally linear embedding (LLE) [2] , isometric feature mapping (ISOMAP) [3] , Laplacian eigenmap [7] , and others [8][9][10][11][12][13] .…”

Section: Introductionmentioning

confidence: 99%

Incremental Alignment Manifold Learning

韩志

孟德宇

徐宗本

et al. 2011

J. Comput. Sci. Technol.

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A new manifold learning method, called incremental alignment method (IAM), is proposed for nonlinear dimensionality reduction of high dimensional data with intrinsic low dimensionality. The main idea is to incrementally align low-dimensional coordinates of input data patch-by-patch to iteratively generate the representation of the entire dataset. The method consists of two major steps, the incremental step and the alignment step. The incremental step incrementally searches neighborhood patch to be aligned in the next step, and the alignment step iteratively aligns the low-dimensional coordinates of the neighborhood patch searched to generate the embeddings of the entire dataset. Compared with the existing manifold learning methods, the proposed method dominates in several aspects: high efficiency, easy out-of-sample extension, well metric-preserving, and averting of the local minima issue. All these properties are supported by a series of experiments performed on the synthetic and real-life datasets. In addition, the computational complexity of the proposed method is analyzed, and its efficiency is theoretically argued and experimentally demonstrated.

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Classification of gene-expression data: The manifold-based metric learning way

Cited by 21 publications

References 22 publications

Pattern classification based on k locally constrained line

Pattern classification based on k locally constrained line

Gene function prediction with knowledge from gene ontology

Incremental Alignment Manifold Learning

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