553009
Adaptive discriminant analysis for microarray-based classification
Microarray technology has generated enormous amounts of high-dimensional gene expression data, providing a unique platform for exploring gene regulatory networks. However, the curse of dimensionality plagues effort to analyze these high throughput data. Linear Discriminant Analysis (LDA) and Biased Discriminant Analysis (BDA) are two popular techniques for dimension reduction, which pay attention to different roles of the positive and negative samples in finding discriminating subspace. However, the drawbacks of these two methods are obvious: LDA has limited efficiency in classifying sample data from subclasses with different distributions, and BDA does not account for the underlying distribution of negative samples.
 In this paper, we propose a novel dimension reduction technique for microarray analysis: Adaptive Discriminant Analysis (ADA), which effectively exploits favorable attributes of both BDA and LDA and avoids their unfavorable ones. ADA can find a good discriminative subspace with adaptation to different sample distributions. It not only alleviates the problem of high dimensionality, but also enhances the classification performance in the subspace with naïve Bayes classifier. To learn the best model fitting the real scenario, boosted Adaptive Discriminant Analysis is further proposed. Extensive experiments on the yeast cell cycle regulation data set, and the expression data of the red blood cell cycle in malaria parasite Plasmodium falciparum demonstrate the superior performance of ADA and boosted ADA. We also present some putative genes of specific functional classes predicted by boosted ADA. Their potential functionality is confirmed by independent predictions based on Gene Ontology, demonstrating that ADA and boosted ADA are effective dimension reduction methods for microarray-based classification.
5390173
A Self-supervised Learning Framework for Classifying Microarray Gene Expression Data
It is important to develop computational methods that can effectively resolve two intrinsic problems in microarray data: high dimensionality and small sample size. In this paper, we propose a self-supervised learning framework for classifying microarray gene expression data using Kernel Discriminant-EM (KDEM) algorithm. This framework applies self-supervised learning techniques in an optimal nonlinear discriminating subspace. It efficiently utilizes a large set of unlabeled data to compensate for the insufficiency of a small set of labeled data and it extends linear algorithm in DEM to kernel algorithm to handle nonlinearly separable data in a lower dimensional space. Extensive experiments on the Plasmodium falciparum expression profiles show the promising performance of the approach.
40832727
Interactive Semisupervised Learning for Microarray Analysis
15273775
Learning Microarray Gene Expression Data by Hybrid Discriminant Analysis
Microarray technology offers a high-throughput means to study expression networks and gene regulatory networks in cells. The intrinsic nature of high dimensionality and small sample size in microarray data calls for effective computational methods. In this article, we propose a novel hybrid dimension reduction technique for classification that combines principal component analysis (PCA) and linear discriminant analysis (LDA)-hybrid PCA and LDA analysis. This technique effectively solves the singular scatter matrix problem caused by small training samples and increases the effective dimension of the projected subspace. It offers more flexibility and a richer set of alternatives to LDA and PCA in the parametric space. In addition, we propose a boosted hybrid discriminant analysis (HDA), using the AdaBoost algorithm which provides a unified and stable solution to find close to the optimal PCA-LDA prediction result and also reduces computational complexity. Extensive experiments on the yeast cell cycle regulation data set show the superior performance of the hybrid analysis, as we explain.
8504259
A Low Complexity MPEG Video Decoder with Arbitrary Downscaling Capability
This paper presents some new techniques to implement arbitrary downscaling in decoding MPEG video with very low complexity. The DCT block recomposition method, which was used in resizing static image, is introduced to downscale video in frequency domain with arbitrary factors. New motion compensation based on the block group motion evaluation is proposed to correspond to the downscaled IDCT. Block groups of regular motion and irregular motion are handled respectively to balance the computation cost and picture quality. Analysis indicates the great complexity reduction and experiments show the decoder performance on the MPEG-4 video sequence. These techniques will be beneficial to the video applications on the mobile devices and many other low end embedded systems
42946887
An Approach to Integrating SIP in Converged Multimodal/Multimedia Communication Services
Abstract
In this paper, we present an approach of integrating SIP (Session Initiation Protocol) in converged multimodal/multimedia communication services. An extensible VoIPTeleserver for VoIP in SIP environment is described. It is based on the concept of dialogue system and Web convergence that separates the channel dependent media resources from the application dependent service creation and hosting environment. It supports XML based service applications for multiple channels including voice, DTMF, IM and chat over IP. The loosely coupled open architecture in our approach is highly extensible. We describe the concept and structure of VoIPTeleServer used in our approach in detail, which interfaces to the VoIP world through SIP signaling and works as a broker between the VoIP SIP environment and MTIP to deliver converged communication services. A prototype of VoIPTeleServer was implemented, and services and applications based on SIP and MTIP convergence are constructed. Special attention is given to the adverse effect of delay, jitter and packet loss for voice portal services over IP. In particular, case studies of DTMF service in voice portal under adverse channel conditions are performed. The compounding effects of multiple channel impairments to DTMF in voice portal services over IP are characterized. The potential high error rate of the DTMF service indicates that the data redundancy method as proposed in RFC 2198 is needed for DTMF in order to achieve reliable voice portal services over IP.

11689877
A Projection and Search Algorithm for the Bicluster Problem
The advent of microarray technologies has made the experimental study of gene expression faster and more efficient. It is an important task to analyze the gene expression data acquired from the microarray experiments. Biclustering is a popular method to search a subset of genes which exhibit similar expression patterns along a subset of conditions in the gene expression matrix. In this paper, we propose a projection and search algorithm (PSA) for finding biclusters in the gene expression matrix. The algorithm adopts projection method to find a raw submatrix, then refines and extends the raw submatrix into a bicluster using greedy algorithms. We test the algorithm on simulation data and real data. The test results show that the algorithm has good performance on both simulation data and real data. Additionally, compared with other methods, PSA isnpsilat sensitive to noise level.
10188810
Estimation Distribution of Algorithm for Fuzzy Clustering Gene Expression Data
With the rapid development of genome projects, clustering of gene expression data is a crucial step in analyzing gene function and relationship of conditions. In this paper, we put forward an estimation of distribution algorithm for fuzzy clustering gene expression data, which combines estimation of distribution algorithms and fuzzy logic. Comparing with sGA, our method can avoid many parameters and can converge quickly. Tests on real data show that EDA converges ten times as fast as sGA does in clustering gene expression data. For clustering accuracy, EDA can get a more reasonable result than sGA does in the worst situations although both methods can get the best results in the best situations.
10172928
Texture-Consistent Shadow Removal
This paper presents an approach to shadow removal that preserves texture consistency between the original shadow and lit area. Illumination reduction in the shadow area not only darkens that area, but also changes the texture characteristics there. We achieve texture-consistent shadow removal by constructing a shadow-free and texture-consistent gradient field. First, we estimate an illumination change surface which causes the shadow and remove the gradients it induces. We approximate the illumination change surface with illumination change splines across the shadow boundary. We formulate estimating these splines as an optimization problem which balances the smoothness between the neighboring splines and their fitness to the image data. Second, we sample the shadow effect on the texture characteristics in the umbra and lit area near the shadow boundary, and remove it by transforming the gradients inside the shadow area to be compatible with the lit area. Experiments on photos from Flickr demonstrate the effectiveness of our method.