4,137 research outputs found
An optimal factor analysis approach to improve the wavelet-based image resolution enhancement techniques
The existing wavelet-based image resolution enhancement techniques have many assumptions, such as limitation of the way to generate low-resolution images and the selection of wavelet functions, which limits their applications in different fields. This paper initially identifies the factors that effectively affect the performance of these techniques and quantitatively evaluates the impact of the existing assumptions. An approach called Optimal Factor Analysis employing the genetic algorithm is then introduced to increase the applicability and fidelity of the existing methods. Moreover, a new Figure of Merit is proposed to assist the selection of parameters and better measure the overall performance. The experimental results show that the proposed approach improves the performance of the selected image resolution enhancement methods and has potential to be extended to other methods
Extended morphometric analysis of neuronal cells with Minkowski valuations
Minkowski valuations provide a systematic framework for quantifying different
aspects of morphology. In this paper we apply vector- and tensor-valued
Minkowski valuations to neuronal cells from the cat's retina in order to
describe their morphological structure in a comprehensive way. We introduce the
framework of Minkowski valuations, discuss their implementation for neuronal
cells and show how they can discriminate between cells of different types.Comment: 14 pages, 18 postscript figure
Bulletin of Mathematical Biology - facts, figures and comparisons
The Society for Mathematical Biology (SMB) owns the Bulletin of Mathematical Biology (BMB). This is an international journal devoted to the interface of mathematics and biology. At the 2003 SMB annual meeting in Dundee the Society asked the editor of the BMB to produce an analysis of impact factor, subject matter of papers, submission rates etc. Other members of the society were interested in the handling times of articles and wanted comparisons with other (appropriate) journals. In this article we present a brief history of the journal and report on how the journal impact factor has grown substantially in the last few years. We also present an analysis of subject areas of published papers over the past two years. We finally present data on times from receipt of paper to acceptance, acceptance to print (and to online publication) and compare these data with some other journals
Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease
The diagnosis of Alzheimer's disease (AD) in routine clinical practice is
most commonly based on subjective clinical interpretations. Quantitative
electroencephalography (QEEG) measures have been shown to reflect
neurodegenerative processes in AD and might qualify as affordable and thereby
widely available markers to facilitate the objectivization of AD assessment.
Here, we present a novel framework combining Riemannian tangent space mapping
and elastic net regression for the development of brain atrophy markers. While
most AD QEEG studies are based on small sample sizes and psychological test
scores as outcome measures, here we train and test our models using data of one
of the largest prospective EEG AD trials ever conducted, including MRI
biomarkers of brain atrophy.Comment: Presented at NIPS 2017 Workshop on Machine Learning for Healt
A Graph-Based Semi-Supervised k Nearest-Neighbor Method for Nonlinear Manifold Distributed Data Classification
Nearest Neighbors (NN) is one of the most widely used supervised
learning algorithms to classify Gaussian distributed data, but it does not
achieve good results when it is applied to nonlinear manifold distributed data,
especially when a very limited amount of labeled samples are available. In this
paper, we propose a new graph-based NN algorithm which can effectively
handle both Gaussian distributed data and nonlinear manifold distributed data.
To achieve this goal, we first propose a constrained Tired Random Walk (TRW) by
constructing an -level nearest-neighbor strengthened tree over the graph,
and then compute a TRW matrix for similarity measurement purposes. After this,
the nearest neighbors are identified according to the TRW matrix and the class
label of a query point is determined by the sum of all the TRW weights of its
nearest neighbors. To deal with online situations, we also propose a new
algorithm to handle sequential samples based a local neighborhood
reconstruction. Comparison experiments are conducted on both synthetic data
sets and real-world data sets to demonstrate the validity of the proposed new
NN algorithm and its improvements to other version of NN algorithms.
Given the widespread appearance of manifold structures in real-world problems
and the popularity of the traditional NN algorithm, the proposed manifold
version NN shows promising potential for classifying manifold-distributed
data.Comment: 32 pages, 12 figures, 7 table
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