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| 1 | Sparse discriminant analysis for breast cancer biomarker identification and classification显示文摘Biomarker identification and cancer classification are two important procedures in microarray data analysis.We propose a novel unified method to carry out both tasks.We first preselect biomarker candidates by eliminating unrelated genes through the BSS/WSS ratio filter to reduce computational cost,and then use a sparse discriminant analysis method for simultaneous biomarker identification and cancer classification.Moreover,we give a mathematical justification about automatic biomarker identification.Experimental results show that the proposed method can identify key genes that have been verified in biochemical or biomedical research and classify the breast cancer type correctly. | Yu Shi Daoqing Dai Chaochun Liu Hong Yan | 2009 | Progress in Natural Science:Materials International2009，19，11： | 4 |
| 2 | TheC∞-convergence of SG circle patterns to the Riemann mapping显示文摘 | Lan Shiyi Dai Daoqing | 2007 | J Math Anal Appl2007，332，： | 1 |
| 3 | Local discriminate wavelet packet coordinates for face recognition显示文摘 | Liu Chaochun Dai Daoqing Yan Hong | 2007 | Learn- ing Research2007，8，5： | 1 |
| 4 | Incremental learning of bidirectional prin- cipal components for face recognition 显示文摘 | Ren Chuanxian Dai Daoqing | 2010 | Pattern Recognition2010，43，1： | 1 |
| 5 | Local dDiscrlminant wavelet packet coordinates for face recognition显示文摘 | LIU CHAOCHUN DAI DAOQING | 2007 | Journal of Machine Learning Research2007，，8： | 1 |
| 6 | Wavelet based discrim inantanalysis for face recognition 显示文摘 | Dai Daoqing YUEN P C | 2006 | Applied Mathematics and Computation2006，175，1： | 1 |
| 7 | Local discriminant wavelet packet coordinates for face reeongnicition显示文摘 | Liu Chaochun Dai Daoqing Yan Hong | 2007 | Learning Research2007，8，： | 1 |
| 8 | The C-infinity-convergence of circle packings of bounded degree to the Riemann mapping显示文摘 | Lan Shiyi Dai Daoqing | 2011 | J Math Anal Appl2011，376，： | 1 |
| 9 | C%convergence of circle patterns to minimal surfaces显示文摘 | Lan Shiyi Dai Daoqing | 2009 | Nagoya Math J2009，194，： | 1 |
| 10 | Local discriminant waveletpacket coordinates for face recognition显示文摘 | Liu Chaochun Dai Daoqing Yan Hong | 2007 | The Journal of MachineLearning Research2007，，8： | 1 |
| 11 | Wavelet based discriminant analysis for face recognition显示文摘 | DAI DAOQING YUEN P C | 2006 | Applied Mathematics and Computation2006，175，1： | 1 |
| 12 | Wavelet based discriminant analysis for face recognition 显示文摘 | DAI Daoqing YUEN P C | 2006 | Applied Mathematics and Com- putation2006，175，1： | 1 |
| 13 | Local discriminant wavelet packet coordinates for face recognition显示文摘 | Liu Chaochun Dai Daoqing Yan Hong | 2007 | The Journal of Machine Learning Research2007，，8： | 1 |
| 14 | Face recognition using dual-tree complex wavelet features 显示文摘 | LIu Chaochun DAI Daoqing | 2009 | IEEE Trans- actions on Image Processing2009，18，11： | 1 |
| 15 | Face Recognition Using Dual-tree Complex Wavelet Features显示文摘 | Liu Chaochun Dai Daoqing | 2009 | IEEE Transactions on Image Processing2009，18，11： | 1 |
| 16 | Structure- d sparse error coding for face recognition with occlusion 显示文摘 | Li Xiaoxin Dai Daoqing Zhang Xiaofei | 2013 | IEEE Transactions on Image Processing2013，22，5： | 1 |
| 17 | Regularized discriminant analysis and its application to face recognition显示文摘 | DAI Daoqing YUEN P C | | 0，，03： | 1 |
| 18 | An adaptive spatial clustering method for automatic brain MR image segmentation显示文摘In this paper,an adaptive spatial clustering method is presented for automatic brain MR image segmentation,which is based on a competitive learning algorithm-self-organizing map(SOM).We use a pattern recognition approach in terms of feature generation and classifier design.Firstly,a multi-dimensional feature vector is constructed using local spatial information.Then,an adaptive spatial growing hierarchical SOM(ASGHSOM) is proposed as the classifier,which is an extension of SOM,fusing multi-scale segmentation with the competitive learning clustering algorithm to overcome the problem of overlapping grey-scale intensities on boundary regions.Furthermore,an adaptive spatial distance is integrated with ASGHSOM,in which local spatial information is considered in the clustering process to reduce the noise effiect and the classification ambiguity.Our proposed method is validated by extensive experiments using both simulated and real MR data with varying noise level,and is compared with the state-of-the-art algorithms. | Jingdan Zhang,Daoqing Dai Center for Computer Vision and Department of Mathematics,Sun Yat-Sen(Zhongshan) University,Guangzhou 510275,China | 2009 | Progress in Natural Science:Materials International2009，19，10： | 0 |
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