The Experts below are selected from a list of 11370 Experts worldwide ranked by ideXlab platform
Guo Hong-tao - One of the best experts on this subject based on the ideXlab platform.
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Web Services Filtrate Technologies Based on Latent Semantic Analysis
Computer Engineering, 2008Co-Authors: Guo Hong-taoAbstract:The available services discovery mechanisms have lower matching efficiency and precision because of lacking services filtrate mechanism and matching the services basic attributes,quality of services attributes by keywords.This paper puts forward a Web services filtrate method based on Latent Semantic Analysis(LSA).This method describes Web services basic attributes,quality attributes using tree structure,uses certain terms-frequency statistic method and weights method to build Latent Semantic Analysis space,and builts advertising services index database and filtrates the Web services according to services request.Experimental results prove that this algorithm has higher precision and recall and improves services matching efficiency largely.
Jonathan I. Maletic - One of the best experts on this subject based on the ideXlab platform.
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Automatic software clustering via Latent Semantic Analysis
2003Co-Authors: Jonathan I. Maletic, N. ValluriAbstract:The paper describes the initial results of applying Latent Semantic Analysis (LSA) to program source code and associated documentation. Latent Semantic Analysis is a corpus based statistical method for inducing and representing aspects of the meanings of words and passages (of natural language) reflective in their usage. This methodology is assessed for application to the domain of software components (i.e., source code and its accompanying documentation). The intent of applying Latent Semantic Analysis to software components is to automatically induce a specific Semantic meaning of a given component. Here LSA is used as the basis to cluster software components. Results of applying this method to the LEDA library and MINIX operating system are given. Applying Latent Semantic Analysis to the domain of source code and internal documentation for the support of software reuse is a new application of this method and a departure from the normal application domain of natural language
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Using Latent Semantic Analysis to identify similarities in source code to support program understanding
Proceedings - International Conference on Tools with Artificial Intelligence ICTAI, 2000Co-Authors: Jonathan I. Maletic, A. MarcusAbstract:The paper describes the results of applying Latent Semantic Analysis (LSA), an advanced information retrieval method, to program source code and associated documentation. Latent Semantic Analysis is a corpus based statistical method for inducing and representing aspects of the meanings of words and passages (of natural language) reflective in their usage. This methodology is assessed for application to the domain of software components (i.e., source code and its accompanying documentation). Here LSA is used as the basis to cluster software components. This clustering is used to assist in the understanding of a nontrivial software system, namely a version of Mosaic. Applying Latent Semantic Analysis to the domain of source code and internal documentation for the support of program understanding is a new application of this method and a departure from the normal application domain of natural language
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ASE - Automatic software clustering via Latent Semantic Analysis
14th IEEE International Conference on Automated Software Engineering, 1Co-Authors: Jonathan I. Maletic, N. ValluriAbstract:The paper describes the initial results of applying Latent Semantic Analysis (LSA) to program source code and associated documentation. Latent Semantic Analysis is a corpus based statistical method for inducing and representing aspects of the meanings of words and passages (of natural language) reflective in their usage. This methodology is assessed for application to the domain of software components (i.e., source code and its accompanying documentation). The intent of applying Latent Semantic Analysis to software components is to automatically induce a specific Semantic meaning of a given component. Here LSA is used as the basis to cluster software components. Results of applying this method to the LEDA library and MINIX operating system are given. Applying Latent Semantic Analysis to the domain of source code and internal documentation for the support of software reuse is a new application of this method and a departure from the normal application domain of natural language.
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ICTAI - Using Latent Semantic Analysis to identify similarities in source code to support program understanding
Proceedings 12th IEEE Internationals Conference on Tools with Artificial Intelligence. ICTAI 2000, 1Co-Authors: Jonathan I. Maletic, A. MarcusAbstract:The paper describes the results of applying Latent Semantic Analysis (LSA), an advanced information retrieval method, to program source code and associated documentation. Latent Semantic Analysis is a corpus based statistical method for inducing and representing aspects of the meanings of words and passages (of natural language) reflective in their usage. This methodology is assessed for application to the domain of software components (i.e., source code and its accompanying documentation). Here LSA is used as the basis to cluster software components. This clustering is used to assist in the understanding of a nontrivial software system, namely a version of Mosaic. Applying Latent Semantic Analysis to the domain of source code and internal documentation for the support of program understanding is a new application of this method and a departure from the normal application domain of natural language.
Suhyin Lee - One of the best experts on this subject based on the ideXlab platform.
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background music recommendation for video based on multimodal Latent Semantic Analysis
International Conference on Multimedia and Expo, 2013Co-Authors: Fangfei Kuo, Mankwan Shan, Suhyin LeeAbstract:Automatic video editing is receiving increasingly attention as the digital camera technology develops further and social media sites such as YouTube and Flickr become popular. Background music selection is one of the key elements to make the generated video attractive. In this work, we propose a framework for background music recommendation based on multi-modal Latent Semantic Analysis between video and music. The videos and accompanied background music are collected from YouTube, and the videos with low musicality are filtered out by musicality detection algorithm. The co-occurrence relationships between audiovisual features are derived for multi-modal Latent Semantic Analysis. Then, given a video, a ranked list of recommended music can be derived from the correlation model. In addition, we propose an algorithm for music beat and video shot alignment to calculate the alignability of recommended music and video. The final recommendation list is the combined result of both content correlation and alignability. Experiments show that the proposed method achieves a promising result.
Li Jianjun - One of the best experts on this subject based on the ideXlab platform.
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Video Retrieval Based on Latent Semantic Analysis
Computer Engineering, 2007Co-Authors: Li JianjunAbstract:Latent Semantic Analysis is based on video Analysis.It constructs the video feature matrix via a kind of mapping and realizes video based content retrieval.The paper discusses the main idea of Latent Semantic Analysis and researches the method color texture extraction.The result proves Latent Semantic Analysis performs preferable in content based video retrieval.
Victor R. Prybutok - One of the best experts on this subject based on the ideXlab platform.
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Latent Semantic Analysis: Five methodological recommendations
European Journal of Information Systems, 2012Co-Authors: Nicholas E. Evangelopoulos, Xiaoni Zhang, Victor R. PrybutokAbstract:The recent influx in generation, storage, and availability of textual data presents researchers with the challenge of developing suitable methods for their Analysis. Latent Semantic Analysis (LSA),...
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Causal Latent Semantic Analysis (cLSA): An Illustration
International Business Research, 2011Co-Authors: Muhammad Muazzem Hossain, Victor R. Prybutok, Nicholas E. EvangelopoulosAbstract:Latent Semantic Analysis (LSA), a mathematical and statistical technique, is used to uncover Latent Semantic structure within a text corpus. It is a methodology that can extract the contextual-usage meaning of words and obtain approximate estimates of meaning similarities among words and text passages. While LSA has a plethora of applications such as natural language processing and library indexing, it lacks the ability to validate models that possess interrelations and/or causal relationships between constructs. The objective of this study is to develop a modified Latent Semantic Analysis called the causal Latent Semantic Analysis (cLSA) that can be used both to uncover the Latent Semantic factors and to establish causal relationships among these factors. The cLSA methodology illustrated in this study will provide academicians with a new approach to test causal models based on quantitative Analysis of the textual data. The managerial implication of this study is that managers can get an aggregated understanding of their business models because the cLSA methodology provides a validation of them based on anecdotal evidence.
