Pattern recognition on seismic data is a useful technique for generating seismic facies maps that capture changes in the geological depositional setting. Seismic facies analysis can be performed using the supervised and unsupervised pattern recognition methods. Each of these methods has its own advantages and disadvantages. In this paper, we compared and evaluated the capability of two unsuperv...

Background: The analysis of epidemiological data at an early phase situation, when the confident correlation contributing factors to outcome has not yet been established, may present a challenge for conventional methods analysis. Objective: This study aimed develop approaches that can be effective in areas with less labeled data. Methods: An combined dataset statistics national and subnational ...

Recently machine learning methodology has been used increasing to analyze the relationship between stimulus categories and fMRI responses [2, 14, 15, 11, 13, 8, 9, 1, 12, 7]. Here, we introduce a new unsupervised machine learning approach to fMRI analysis approach, in which the simple categorical description of stimulus type (e.g. type of task) is replaced by a more informative vector of stimul...

In much of the analysis of high-throughput genomic data, "interesting" genes have been selected based on assessment of differential expression between two groups or generalizations thereof. Most of the literature focuses on changes in mean expression or the entire distribution. In this article, we explore the use of C(α) tests, which have been applied in other genomic data settings. Their use f...

Linking between two data sources is a basic building block in numerous computer vision problems. In this paper, we set to answer a fundamental cognitive question: are prior correspondences necessary for linking between different domains? One of the most popular methods for linking between domains is Canonical Correlation Analysis (CCA). All current CCA algorithms require correspondences between...