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Feature selection methods have been widely used in gene expression analysis to identify differentially expressed genes and explore potential biomarkers for complex diseases. While a lot of studies have shown that incorporating feature structure information can greatly enhance the performance of feature selection algorithms, and genes naturally fall into groups with regard to common function and co-regulation,...
With the rapid development of artificial intelligence and natural language processing, text similarity calculation has become the core module of many applications such as semantic disambiguation, information retrieval, automatic question answering and data mining etc. Most of the existing semantic similarity algorithms are based on statistical methods or rule based methods that are conducted on ontology...
With the fast growing development of the Web, the adoption of ontologies to improve the exploitation of information resources, is already heralded as a promising model of representation. However, the relevance of information that they contain requires regular updating, and specifically, the addition of new knowledge. Recently, new research approaches were defined in order to automatically enrich ontology...
Automatic image annotation is a promising solution to enable more effective image retrieval by keywords. Different statistical models and machine learning methods have been introduced for image auto-annotation. In this paper, we propose a collaborative approach, in which multiple different statistical models are combined effectively to predict the annotation for each image. Moreover, we combine both...
The inter-language studies on the textual semantic accessibility scale (SAS) are a new branch of the computational linguistics and the present paper tries to statistically probe into the SASes in English, French and Japanese literature works sampled from the corresponding corpora. Firstly, six control groups are formed by the equidistant texts extracted every 10 pages, 5 pages, 4 pages, 3 pages, 2...
Automatic image tagging (AIT) is an effective technology to facilitate the process of image retrieval without requiring user to provide a retrieval instance beforehand. In this paper, we propose an AIT method based on kernel canonical correlation analysis (KCCA) with similarity refinement (KCCSR). As a statistic correlation technique, the KCCA aims at extracting some kind of hidden information shared...
Image retrieval is one of the hottest fields of computer vision and pattern recognition. In recent years, many researchers addressed the challenging problem of interpreting the semantics of images. This paper presented a novel approach based on relation net (concept and semantic keyword relation net) for high level semantic retrieval of Thangka image. Here, we use Delphi method and fuzzy statistic...
The measurement of similarity between documents is usually influenced by sparseness of term-document matrix. Latent semantic indexing (LSI) is an alternative method to solve the problem, and the dimension reduction by LSI improves the performance of the measurement of the similarity. In this study, LSI is examined as a method to cluster clinical documents containing the same clinical problems or disorders...
In this paper, we propose a novel method to measure the semantic similarity between genes. The key principle of our method relies on both path length between genes' annotation terms in the Gene Ontology and depth of their annotation terms' common ancestor node in the Gene Ontology. Our method applies an exponential transfer function which includes path length and depth as its two parameters to get...
The problem of matching samples between two data sets is a fundamental task in unsupervised learning. In this paper we propose an algorithm based on statistical dependency between the data sets to solve the matching problem in a general case when samples in both data sets have different feature representations. As a concrete example, we consider the task of sentence-level alignment of parallel corpus...
A correlation-enhanced similarity matching framework for medical image retrieval is presented in a local concept-based feature space. In this framework, images are presented by vectors of concepts that comprise of local color and texture patches of image regions in a multi-dimensional feature space. To generate the concept vocabularies and represent the images, statistical models are built using a...
By using the remote functions of a modern IT service management system infrastructure, it is possible to analyze huge amounts of log file data from complex technical equipment. This enables a service provider to predict failures of connected equipment before they happen. The problem most providers face in this context is finding "a needle in a haystack" - the obtained amount of data turns...
Does there exist a compact set of visual topics in form of keyword clusters capable to represent all images visual content within an acceptable error? In this paper, we answer this question by analyzing distribution laws for keywords from image descriptions and comparing with traditional techniques in NLP, thereby propose three assumptions: Sparse Distribution Attribute, Local Convergent Assumption...
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