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.
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])
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.
3147895
Pitfalls in public key watermarking
The cryptographic concept of digital signatures is well suited to image watermarking for authentication and integrity verification. However, the peculiar nature of watermarks demands special care in the application of digital signatures for this purpose. We cryptanalyze a recently proposed watermarking scheme and show how it can be strengthened to thwart our attacks, exploring the use of hashing function contexts and signature algorithms based on elliptic curve cryptography.
14176330
System Specification Based Network Modeling for Survivability Testing Simulation
As the structure and behavior of computer network becomes complicated and unpredictable, it becomes difficult to test the survivability of network. The modeling and simulation is a widely used approach to predict the behavior of system or set of system. In this research, we apply a modeling methodology to construct the valid model of computer network focusing on its vulnerability for survivability testing. To accomplish our aim, the appropriate modeling method should be defined. Especially, we take advantage of the system specification based modeling approach to construct valid network model.
16590165
New Public-Key Authentication Watermarking for JBIG2 Resistant to Parity Attacks
An authentication watermark is a hidden data inserted into an image that allows detecting any alteration made in the image. AWTs (Authentication Watermarking Techniques) normally make use of secret- or public-key cryptographic cipher to compute the authentication signature of the image, and inserts it into the image itself. Many previous public-key AWTs for uncompressed binary images can be attacked by an image adulterating technique named “parity attack.” JBIG2 is an international standard for compressing bi-level images (both lossy and lossless). The creation of secure AWTs for compressed binary images is an important practical problem. However, it seems that no AWT for JBIG2 resistant to parity attacks has ever been proposed. This paper proposes a new data-hiding method to embed information in the text region of JBIG2 files. Then, we use this technique to design a new AWT for JBIG2-encoded images resistant to parity attacks. Both the secret- and public-key versions of the proposed AWT are completely immune against parity attacks. Moreover, watermarked images are visually pleasant, without visible salt and pepper noise. Image authenticity verification can be performed in either JBIG2 file itself or in the binary image obtained by decoding the JBIG2 file.
4651323
Face description based on decomposition and combining of a facial space with LDA
We propose a method of efficient face description for facial image retrieval from a large data set. The novel descriptor is obtained by decomposing the face image into several components and then combining the component features. The decomposition combined with LDA (linear discriminant analysis) provides discriminative facial features that are less sensitive to light and pose changes. Each component is represented in its Fisher space and another LDA is then applied to compactly combine the features of the components. To enhance retrieval accuracy further, a simple pose classification and transformation technique is performed, followed by recursive matching. The experimental results obtained on the MPEG-7 data set show an impressive accuracy of our algorithm as compared with the conventional PCA/ICA/LDA methods.
17092169
Robust anisotropic diffusion to produce enhanced statistical parametric map from noisy fMRI
This paper presents a new, simple, and elegant technique to obtain enhanced statistical parametric maps (SPMs) from noisy functional magnetic resonance imaging (fMRI) data. This technique is based on the robust anisotropic diffusion (RAD), a technique normally used as an edge-preserving filter. A direct application of the RAD to the fMRI data does not work, because in this case RAD would perform an edge-preserving filtering of the fMRI structural information, instead of enhancing its functional information. The RAD can be applied directly to SPM but, in this case, only a small improvement of the SPM quality can be achieved, because the originating fMRI is not taken into account. To overcome these difficulties, we propose to estimate the SPM from the noisy fMRI, compute the diffusion coefficients in the SPM space, and then perform the diffusion in the structural information-removed fMRI data using the coefficients previously computed. These steps are iterated until convergence. We have tested the new technique in both simulated and real fMRI images, yielding surprisingly sharp and noiseless SPMs with increased statistical significance. We also describe how to automatically estimate an appropriate scale parameter.