27,507 research outputs found

    Sparse Correlation Kernel Analysis and Reconstruction

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    This paper presents a new paradigm for signal reconstruction and superresolution, Correlation Kernel Analysis (CKA), that is based on the selection of a sparse set of bases from a large dictionary of class- specific basis functions. The basis functions that we use are the correlation functions of the class of signals we are analyzing. To choose the appropriate features from this large dictionary, we use Support Vector Machine (SVM) regression and compare this to traditional Principal Component Analysis (PCA) for the tasks of signal reconstruction, superresolution, and compression. The testbed we use in this paper is a set of images of pedestrians. This paper also presents results of experiments in which we use a dictionary of multiscale basis functions and then use Basis Pursuit De-Noising to obtain a sparse, multiscale approximation of a signal. The results are analyzed and we conclude that 1) when used with a sparse representation technique, the correlation function is an effective kernel for image reconstruction and superresolution, 2) for image compression, PCA and SVM have different tradeoffs, depending on the particular metric that is used to evaluate the results, 3) in sparse representation techniques, L_1 is not a good proxy for the true measure of sparsity, L_0, and 4) the L_epsilon norm may be a better error metric for image reconstruction and compression than the L_2 norm, though the exact psychophysical metric should take into account high order structure in images

    Influence of Dictionary Size on the Lossless Compression of Microarray Images

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    A key challenge in the management of microarray data is the large size of images that constitute the output of microarray experiments. Therefore, only the expression values extracted from these experiments are generally made available. However, the extraction of expression data is effected by a variety of factors, such as the thresholds used for background intensity correction, method used for grid determination, and parameters used in foreground (spot)-background delineation. This information is not always available or consistent across experiments and impacts downstream data analysis. Furthermore, the lack of access to the image-based primary data often leads to costly replication of experiments. Currently, both lossy and lossless compression techniques have been developed for microarray images. While lossy algorithms deliver better compression, a significant advantage of the lossless techniques is that they guarantee against loss of information that is putatively of biological importance. A key challenge therefore is the development of more efficacious lossless compression techniques. Dictionary-based compression is one of the critical methods used in lossless microarray compression. However, the image-based microarray data has potentially infinite variability. So the selection and effect of the dictionary size on the compression rate is crucial. Our paper examines this problem and shows that increasing the dictionary size beyond a certain size, does not lead to better compression. Our investigations also point to strategies for determining the optimal dictionary size. 1
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