558514
Missing value estimation for DNA microarray gene expression data: local least squares imputation
MOTIVATION
Gene expression data often contain missing expression values. Effective missing value estimation methods are needed since many algorithms for gene expression data analysis require a complete matrix of gene array values. In this paper, imputation methods based on the least squares formulation are proposed to estimate missing values in the gene expression data, which exploit local similarity structures in the data as well as least squares optimization process.


RESULTS
The proposed local least squares imputation method (LLSimpute) represents a target gene that has missing values as a linear combination of similar genes. The similar genes are chosen by k-nearest neighbors or k coherent genes that have large absolute values of Pearson correlation coefficients. Non-parametric missing values estimation method of LLSimpute are designed by introducing an automatic k-value estimator. In our experiments, the proposed LLSimpute method shows competitive results when compared with other imputation methods for missing value estimation on various datasets and percentages of missing values in the data.


AVAILABILITY
The software is available at http://www.cs.umn.edu/~hskim/tools.html


CONTACT
hpark@cs.umn.edu
14943272
Incremental and Decremental Least Squares Support Vector Machine and Its Application to Drug Design
The least squares support vector machine (LS-SVM) has shown to exhibit excellent classification performance in many applications. In this paper, we propose an incremental and decremental LS-SVM based on updating and downdating the QR decomposition. It can efficiently compute the updated solution when data points are appended or removed. The experiment results illustrated that the proposed incremental algorithm efficiently produces the same solutions as those obtained by LS-SVM which recomputes the solution all over even for small changes in the data. For drug design, the results of each biochemistry laboratory test on a new compound can be iteratively included in the training set. This procedure can further improve precision in order to select the next best predicted organic compound. Instead of retraining entire data points, it is much efficient to update solution by incremental LS-SVM.
11702143
Adaptive nonlinear discriminant analysis by regularized minimum squared errors
Kernelized nonlinear extensions of Fisher's discriminant analysis, discriminant analysis based on generalized singular value decomposition (LDA/GSVD), and discriminant analysis based on the minimum squared error formulation (MSE) have recently been widely utilized for handling undersampled high-dimensional problems and nonlinearly separable data sets. As the data sets are modified from incorporating new data points and deleting obsolete data points, there is a need to develop efficient updating and downdating algorithms for these methods to avoid expensive recomputation of the solution from scratch. In this paper, an efficient algorithm for adaptive linear and nonlinear kernel discriminant analysis based on regularized MSE, called adaptive KDA/RMSE, is proposed. In adaptive KDA/RMSE, updating and downdating of the computationally expensive eigenvalue decomposition (EVD) or singular value decomposition (SVD) is approximated by updating and downdating of the QR decomposition achieving an order of magnitude speed up. This fast algorithm for adaptive kernelized discriminant analysis is designed by utilizing regularization techniques and the relationship between linear and nonlinear discriminant analysis and the MSE. In addition, an efficient algorithm to compute leave-one-out cross validation is also introduced by utilizing downdating of KDA/RMSE.
8166541
Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis
MOTIVATION
Many practical pattern recognition problems require non-negativity constraints. For example, pixels in digital images and chemical concentrations in bioinformatics are non-negative. Sparse non-negative matrix factorizations (NMFs) are useful when the degree of sparseness in the non-negative basis matrix or the non-negative coefficient matrix in an NMF needs to be controlled in approximating high-dimensional data in a lower dimensional space.


RESULTS
In this article, we introduce a novel formulation of sparse NMF and show how the new formulation leads to a convergent sparse NMF algorithm via alternating non-negativity-constrained least squares. We apply our sparse NMF algorithm to cancer-class discovery and gene expression data analysis and offer biological analysis of the results obtained. Our experimental results illustrate that the proposed sparse NMF algorithm often achieves better clustering performance with shorter computing time compared to other existing NMF algorithms.


AVAILABILITY
The software is available as supplementary material.
1329357
Data Reduction in Support Vector Machines by a Kernelized Ionic Interaction Model
A major drawback of support vector machines is their high computational complexity. In this paper, we introduce a novel kernelized ionic interaction (IoI) model for data reduction in support vector machines. We also present a data reduction method based on the kernelized instance based (KIB2) algorithm. We show that the computation time can be significantly reduced without any significant decrease in the prediction accuracy.
5469426
Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae
BackgroundGene expression and transcription factor (TF) binding data have been used to reveal gene transcriptional regulatory networks. Existing knowledge of gene regulation can be presented using gene connectivity networks. However, these composite connectivity networks do not specify the range of biological conditions of the activity of each link in the network.ResultsWe present a novel method that utilizes the expression and binding patterns of the neighboring nodes of each link in existing experimentally-based, literature-derived gene transcriptional regulatory networks and extend them in silico using TF-gene binding motifs and a compendium of large expression data from Saccharomyces cerevisiae. Using this method, we predict several hundreds of new transcriptional regulatory TF-gene links, along with experimental conditions in which known and predicted links become active. This approach unravels new links in the yeast gene transcriptional regulatory network by utilizing the known transcriptional regulatory interactions, and is particularly useful for breaking down the composite transcriptional regulatory network to condition specific networks.ConclusionOur methods can facilitate future binding experiments, as they can considerably help focus on the TFs that must be surveyed to understand gene regulation.(Supplemental material and the latest version of the MATLAB implementation of the United Signature Algorithm is available online at [1] or [see Additional files 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
26568006
Defense Mechanism for Information and Communication Infrastructure
2343440
Component-based LDA Face Descriptor for Image Retrieval
We present a component-based face descriptor with LDA (Linear Discriminant Analysis) and a simple pose classification. Our algorithm has been developed to deal with face image retrieval in huge database such as those in internet environments. Such retrieval requires a compact face descriptor and an efficient recognition algorithm that is robust to variations in lighting and facial poses. Partitioning of a face image into components facilitates the development of an efficient and robust algorithm as follows. First, compensation for light and pose variations is much more easily done on individual components than on the whole image. Second, pose variation is compensated by classifying facial pose and aligning facial components. Finally, LDA is more effective at the component level which has simplified statistics than the whole image. Experimental results on MPEG-7 database show an impressive accuracy of our algorithm compared with conventional LDA methods.
4633793
Independent component analysis in a local facial residue space for face recognition
In this paper, we propose an Independent Component Analysis (ICA) based face recognition algorithm, which is robust to illumination andpose variation. Generally, it is well known that the 5rst few eigenfaces represent illumination variation rather than identity. Most Principal Component Analysis (PCA) based methods have overcome illumination variation by discarding the projection to a few leading eigenfaces. The space spanned after removing a few leading eigenfaces is called the “residual face space”. We found that ICA in the residual face space provides more e9cient encoding in terms of redundancy reduction androbustness to pose variation as well as illumination variation, owing to its ability to represent non-Gaussian statistics. Moreover, a face image is separatedinto several facial components, local spaces, andeach local space is representedby the ICA bases (independent components) of its corresponding residual space. The statistical models of face images in local spaces are relatively simple andfacilitate classi5cation by a linear encoding. Various experimental results show that the accuracy of face recognition is signi5cantly improvedby the proposedmethodunder large illumination andpose variations.
4665838
Independent component analysis in a facial local residue space
In this paper, we propose an ICA (Independent Component Analysis) based face recognition algorithm, which is robust to illumination and pose variation. Generally, it is well known that the first few eigenfaces represent illumination variation rather than identity. Most PCA (Principal Component Analysis)-based methods have overcome illumination variation by discarding the projection to a few leading eigenfaces. The space spanned after removing a few leading eigenfaces is called the "residual face space". We found that ICA in the residual face space provides more efficient encoding in terms of redundancy reduction and robustness to pose variation as well as illumination variation, owing to its ability to represent non-Gaussian statistics. Moreover, a face image is separated into several facial components, local spaces, and each local space is represented by the ICA bases (independent components) of its corresponding residual space. The statistical models of face images in local spaces are relatively simple and facilitate classification by a linear encoding. Various experimental results show that the accuracy of face recognition is significantly improved by the proposed method under large illumination and pose variations.
15273022
Open Location-Based Service Using Secure Middleware Infrastructure in Web Services
Location-based services or LBS refers to value-added service by processing information utilizing mobile user location. For this kind of LBS, the role of security service is very important in the LBS that store and manage the location information of mobile devices and support various application services using those location information. And in all phases of these functions that include acquisition of location information, storage and management of location information, user management including authentication and information security, and management of the large-capacity location information database, safe security service must be provided. We show the security methods for open LBS in this paper.