2963516
Clustering genes using gene expression and text literature data
Clustering of gene expression data is a standard technique used to identify closely related genes. In this paper, we develop a new clustering algorithm, MSC (Multi-Source Clustering), to perform exploratory analysis using two or more diverse sources of data. In particular, we investigate the problem of improving the clustering by integrating information obtained from gene expression data with knowledge extracted from biomedical text literature. In each iteration of algorithm MSC, an EM-type procedure is employed to bootstrap the model obtained from one data source by starting with the cluster assignments obtained in the previous iteration using the other data sources. Upon convergence, the two individual models are used to construct the final cluster assignment. We compare the results of algorithm MSC for two data sources with the results obtained when the clustering is applied on the two sources of data separately. We also compare it with that obtained using the feature level integration method that performs the clustering after simply concatenating the features obtained from the two data sources. We show that the z-scores of the clustering results from MSC are better than that from the other methods. To evaluate our clusters better, function enrichment results are presented using terms from the Gene Ontology database. Finally, by investigating the success of motif detection programs that use the clusters, we show that our approach integrating gene expression data and text data reveals clusters that are biologically more meaningful than those identified using gene expression data alone.
14819927
Gene functional classification by semi-supervised learning from heterogeneous data
Gene function discovery is an important and interesting problem in computational analysis of microarray data. In this paper, we investigate the use of a semi-supervised learning algorithm for inferring gene functional classifications from heterogeneous data set consisting of DNA microarray expression measurements and phylogenetic profiles from whole-genome sequence compassions. The semisupervised learning approach aims at minimizing the disagreement between individual models built from each separate information source by employing a co-updating method and making use of both labeled and unlabeled data. Our results suggest that the semisupervised approach could be used for gene functional classification. The data sets and the program code used for the experiments can be accessed from our webpage.
1750917
A general model for clustering binary data
Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. This paper studies the problem of clustering binary data. This is the case for market basket datasets where the transactions contain items and for document datasets where the documents contain "bag of words". The contribution of the paper is three-fold. First a general binary data clustering model is presented. The model treats the data and features equally, based on their symmetric association relations, and explicitly describes the data assignments as well as feature assignments. We characterize several variations with different optimization procedures for the general model. Second, we also establish the connections between our clustering model with other existing clustering methods. Third, we also discuss the problem for determining the number of clusters for binary clustering. Experimental results show the effectiveness of the proposed clustering model.
206784011
The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering
The nonnegative matrix factorization (NMF) has been shown recently to be useful for clustering and various extensions and variations of NMF have been proposed recently. Despite significant research progress in this area, few attempts have been made to establish the connections between various factorization methods while highlighting their differences. In this paper we aim to provide a comprehensive study on matrix factorization for clustering. In particular, we present an overview and summary on various matrix factorization algorithms and theoretically analyze the relationships among them. Experiments are also conducted to empirically evaluate and compare various factorization methods. In addition, our study also answers several previously unaddressed yet important questions for matrix factorizations including the interpretation and normalization of cluster posterior and the benefits and evaluation of simultaneous clustering. We expect our study would provide good insights on matrix factorization research for clustering.
9813108
CoFD : An Algorithm for Non-distance Based Clustering in High Dimensional Spaces
The clustering problem, which aims at identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity clusters, has been widely studied. Traditional clustering algorithms use distance functions to measure similarity and are not suitable for high dimensional spaces. In this paper, we propose CoFD algorithm, which is a non-distance based clustering algorithm for high dimensional spaces. Based on the maximum likelihood principle, CoFD is to optimize parameters to maximize the likelihood between data points and the model generated by the parameters. Experimental results on both synthetic data sets and a real data set show the efficiency and effectiveness of CoFD.
8864863
Integrating Features from Different Sources for Music Information Retrieval
Efficient and intelligent music information retrieval is a very important topic of the 21st century. With the ultimate goal of building personal music information retrieval systems, this paper studies the problem of identifying "similar" artists using both lyrics and acoustic data. In this paper, we present a clustering algorithm that integrates features from both sources to perform bimodal learning. The algorithm is tested on a data set consisting of 570 songs from 53 albums of 41 artists using artist similarity provided by All Music Guide. Experimental results show that the accuracy of artist similarity classifiers can be significantly improved and that artist similarity can be efficiently identified.
8716577
Blind identification and deconvolution of linear systems driven by binary random sequences
The problem of blind identification and deconvolution of linear systems with independent binary inputs is addressed. To solve the problem, a linear system is applied to the observed data and adjusted so as to produce binary outputs. It is proved that the system coincides with the inverse of the unknown system (with scale and shift ambiguities), whether it is minimum or nonminimum phase. These results are derived for nonstationary independent binary inputs of infinite or finite length. Based on these results, an identification method is proposed for parametric linear systems. It is shown that under some mild conditions, a consistent estimator of the parameter can be obtained by minimizing a binariness criterion for the output data. Unlike many other blind identification and deconvolution methods, this criterion handles nonstationary signals and does not utilize any moment information of the inputs. Three numerical examples are presented to demonstrate the effectiveness of the proposed method. >
23492812
Singularity analysis of redundant parallel manipulators
The general methods of singularity analysis of a general parallel manipulator are not suitable for that of a redundant parallel manipulator, thereby This work presents a method for the identification of the singularities of redundant parallel manipulators. Firstly, the theory of singular value decomposition is employed to analyze the velocity equation of a mechanism, and the relationship between the generalized input velocities and the generalized output velocities is researched. Then the physical interpretation of the singularity of a redundant parallel manipulator is discussed, and the relationship between the singularity and the singularity values of the Jacobian matrix of a mechanism is studied. Finally, based on above analysis, the singularities of a redundant manipulator are divided into three basic types and a method for computing the singularity set is presented and formulated. As an example, a 3 degree-of-freedom redundant manipulator is researched through using this method, and all the singular configurations of this manipulator are found out and pictured.
25228668
A joint estimation approach for two-tone image deblurring by blind deconvolution
A new statistical method is proposed for deblurring two-tone images, i.e., images with two unknown grey levels, that are blurred by an unknown linear filter. The key idea of the proposed method is to adjust a deblurring filter until its output becomes two tone. Two optimization criteria are proposed for the adjustment of the deblurring filter. A three-step iterative algorithm (TSIA) is also proposed to minimize the criteria. It is proven mathematically that by minimizing either of the criteria, the original (nonblurred) image, along with the blur filter, will be recovered uniquely (only with possible scale/shift ambiguities) at high SNR. The recovery is guaranteed not only for i.i.d. images but also for correlated and nonstationary images. It does not require a priori knowledge of the statistical parameters or the tone values of the original image; neither does it require a priori knowledge of the phase or other special information (e.g., FIR, symmetry, nonnegativity, etc.) about the blur filter. Numerical experiments are carried out to test the method on synthetic and real images.
42024829
Strong consistency of the contraction mapping method for frequency estimation
Some statistical properties in regard to the contraction mapping (CM) method are discussed. One of the requirements in this method is that the filter be parameterized to satisfy a certain fundamental property. The parameterization clearly depends on the normalized noise spectrum which theoretically has to be known or estimated a priori. If this information is available, one can first whiten the noise with a linear filter and then apply the CM method to the filtered data. In this way, the parameterization only needs to be done under the white noise assumption and filters like the AR(2) can be used by the CM method. In applications, however, prewhitening may not always be necessary. >
3016046
Asymptotic analysis of a fast algorithm for efficient multiple frequency estimation