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Journal articles on the topic 'Técnica Probabilistic Latent Semantic Indexing'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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1

Hofmann, Thomas. "Probabilistic Latent Semantic Indexing." ACM SIGIR Forum 51, no. 2 (2017): 211–18. http://dx.doi.org/10.1145/3130348.3130370.

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2

Papadimitriou, Christos H., Prabhakar Raghavan, Hisao Tamaki, and Santosh Vempala. "Latent Semantic Indexing: A Probabilistic Analysis." Journal of Computer and System Sciences 61, no. 2 (2000): 217–35. http://dx.doi.org/10.1006/jcss.2000.1711.

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3

Ding, Chris H. Q. "A probabilistic model for Latent Semantic Indexing." Journal of the American Society for Information Science and Technology 56, no. 6 (2005): 597–608. http://dx.doi.org/10.1002/asi.20148.

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4

Kouassi, Eli Koffi, Toshiyuki Amagasa, and Hiroyuki Kitagawa. "Efficient Probabilistic Latent Semantic Indexing using Graphics Processing Unit." Procedia Computer Science 4 (2011): 382–91. http://dx.doi.org/10.1016/j.procs.2011.04.040.

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5

Tamari, Yuki, Takashi Ideno, and Kazuhisa Takemura. "Estimating decision strategies by probabilistic latent semantic indexing and simulation." Proceedings of the Annual Convention of the Japanese Psychological Association 82 (September 25, 2018): 2PM—015–2PM—015. http://dx.doi.org/10.4992/pacjpa.82.0_2pm-015.

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6

Zhang, Zhong-Yuan, Tao Li, Chris Ding, and Jie Tang. "An NMF-framework for Unifying Posterior Probabilistic Clustering and Probabilistic Latent Semantic Indexing." Communications in Statistics - Theory and Methods 43, no. 19 (2014): 4011–24. http://dx.doi.org/10.1080/03610926.2012.714034.

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7

YANG, Jianxiong, and Junzo WATADA. "A Clustering Method for Web Mining Based on Probabilistic Latent Semantic Indexing." SICE Journal of Control, Measurement, and System Integration 5, no. 5 (2012): 290–95. http://dx.doi.org/10.9746/jcmsi.5.290.

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8

Shibayama, Naoki, and Hiroshi Nakagawa. "Introducing off-diagonal elements to singular value matrix in probabilistic Latent Semantic Indexing." Transactions of the Japanese Society for Artificial Intelligence 26 (2011): 262–72. http://dx.doi.org/10.1527/tjsai.26.262.

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9

ZhongYuan Zhang, Chris Ding, and Jie Tang. "Note on Algorithm Differences Between Nonnegative Matrix Factorization And Probabilistic Latent Semantic Indexing." Journal of Convergence Information Technology 6, no. 9 (2011): 210–19. http://dx.doi.org/10.4156/jcit.vol6.issue9.25.

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10

Ding, Chris, Tao Li, and Wei Peng. "On the equivalence between Non-negative Matrix Factorization and Probabilistic Latent Semantic Indexing." Computational Statistics & Data Analysis 52, no. 8 (2008): 3913–27. http://dx.doi.org/10.1016/j.csda.2008.01.011.

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11

Devarajan, Karthik, Guoli Wang, and Nader Ebrahimi. "A unified statistical approach to non-negative matrix factorization and probabilistic latent semantic indexing." Machine Learning 99, no. 1 (2014): 137–63. http://dx.doi.org/10.1007/s10994-014-5470-z.

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12

Rusakovica, J., J. Hallinan, A. Wipat, and P. Zuliani. "Probabilistic Latent Semantic Analysis Applied to Whole Bacterial Genomes Identifies Common Genomic Features." Journal of Integrative Bioinformatics 11, no. 2 (2014): 93–105. http://dx.doi.org/10.1515/jib-2014-243.

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Summary The spread of drug resistance amongst clinically-important bacteria is a serious, and growing, problem [1]. However, the analysis of entire genomes requires considerable computational effort, usually including the assembly of the genome and subsequent identification of genes known to be important in pathology. An alternative approach is to use computational algorithms to identify genomic differences between pathogenic and non-pathogenic bacteria, even without knowing the biological meaning of those differences. To overcome this problem, a range of techniques for dimensionality reduction have been developed. One such approach is known as latent-variable models [2]. In latent-variable models dimensionality reduction is achieved by representing a high-dimensional data by a few hidden or latent variables, which are not directly observed but inferred from the observed variables present in the model. Probabilistic Latent Semantic Indexing (PLSA) is an extention of LSA [3]. PLSA is based on a mixture decomposition derived from a latent class model. The main objective of the algorithm, as in LSA, is to represent high-dimensional co-occurrence information in a lower-dimensional way in order to discover the hidden semantic structure of the data using a probabilistic framework. In this work we applied the PLSA approach to analyse the common genomic features in methicillin resistant Staphylococcus aureus, using tokens derived from amino acid sequences rather than DNA. We characterised genome-scale amino acid sequences in terms of their components, and then investigated the relationships between genomes and tokens and the phenotypes they generated. As a control we used the non-pathogenic model Gram-positive bacterium Bacillus subtilis.

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13

Kitajima, Risa, and Ichiro Kobayashi. "Latent Topic Estimation Based on Events in a Document." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 5 (2012): 603–10. http://dx.doi.org/10.20965/jaciii.2012.p0603.

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Several latent topic model-based methods such as Latent Semantic Indexing (LSI), Probabilistic LSI (pLSI), and Latent Dirichlet Allocation (LDA) have been widely used for text analysis. These methods basically assign topics to words, however, and the relationship between words in a document is therefore not considered. Considering this, we propose a latent topic extraction method that assigns topics to events that represent the relation between words in a document. There are several ways to express events, and the accuracy of estimating latent topics differs depending on the definition of an event. We therefore propose five event types and examine which event type works well in estimating latent topics in a document with a common document retrieval task. As an application of our proposed method, we also show multidocument summarization based on latent topics. Through these experiments, we have confirmed that our proposed method results in higher accuracy than the conventional method.

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14

Sun, Xiaobing, Xiangyue Liu, Bin Li, Bixin Li, David Lo, and Lingzhi Liao. "Clustering Classes in Packages for Program Comprehension." Scientific Programming 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/3787053.

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During software maintenance and evolution, one of the important tasks faced by developers is to understand a system quickly and accurately. With the increasing size and complexity of an evolving system, program comprehension becomes an increasingly difficult activity. Given a target system for comprehension, developers may first focus on the package comprehension. The packages in the system are of different sizes. For small-sized packages in the system, developers can easily comprehend them. However, for large-sized packages, they are difficult to understand. In this article, we focus on understanding these large-sized packages and propose a novel program comprehension approach for large-sized packages, which utilizes the Latent Dirichlet Allocation (LDA) model to cluster large-sized packages. Thus, these large-sized packages are separated as small-sized clusters, which are easier for developers to comprehend. Empirical studies on four real-world software projects demonstrate the effectiveness of our approach. The results show that the effectiveness of our approach is better than Latent Semantic Indexing- (LSI-) and Probabilistic Latent Semantic Analysis- (PLSA-) based clustering approaches. In addition, we find that the topic that labels each cluster is useful for program comprehension.

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15

Cao, Yu, Shawn Steffey, Jianbiao He, et al. "Medical Image Retrieval: A Multimodal Approach." Cancer Informatics 13s3 (January 2014): CIN.S14053. http://dx.doi.org/10.4137/cin.s14053.

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Medical imaging is becoming a vital component of war on cancer. Tremendous amounts of medical image data are captured and recorded in a digital format during cancer care and cancer research. Facing such an unprecedented volume of image data with heterogeneous image modalities, it is necessary to develop effective and efficient content-based medical image retrieval systems for cancer clinical practice and research. While substantial progress has been made in different areas of content-based image retrieval (CBIR) research, direct applications of existing CBIR techniques to the medical images produced unsatisfactory results, because of the unique characteristics of medical images. In this paper, we develop a new multimodal medical image retrieval approach based on the recent advances in the statistical graphic model and deep learning. Specifically, we first investigate a new extended probabilistic Latent Semantic Analysis model to integrate the visual and textual information from medical images to bridge the semantic gap. We then develop a new deep Boltzmann machine-based multimodal learning model to learn the joint density model from multimodal information in order to derive the missing modality. Experimental results with large volume of real-world medical images have shown that our new approach is a promising solution for the next-generation medical imaging indexing and retrieval system.

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16

Kaneko, Hiroyuki, and Toshihiro Osaragi. "Extraction of The Spatio-temporal Activity Patterns Using Laser-scanner Trajectory Data." AGILE: GIScience Series 1 (July 15, 2020): 1–20. http://dx.doi.org/10.5194/agile-giss-1-9-2020.

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Abstract. A pedestrian tracking system on highly accurate laser scanners is an effective method to understand the usage of the facility space. While this system is capable of gathering an enormous volume of tracking data, specialized skills and significant amounts of labor are needed to get a reliable bird’s-eye view of the spatio-temporal characteristics of the observed data. In this paper, two methods to extract patterns of spatio-temporal activity are described. These can provide a broad overview of the office-worker’s activities in the office throughout a workday and an easily under-stood visualization that indicates what time segment, what location and what activities are taking place. One is a time segment extraction model that identifies characteristic time intervals in the time series data of office-worker’s activities using a classification model based on information loss minimization model. The other is a day scene extraction model that identifies daily scenes from simultaneous behavior patterns in spatio-temporal distributions using a latent class model with PLSI (Probabilistic latent semantic indexing). These methods provide viewpoints for separating their activities of a workday into time segments of appropriate size in order to obtain a grasp of how the activities vary with the time of day. Simultaneous behavior patterns in time, space, and activity are extracted, thereby allowing representation of typical scenes such as morning meetings and extended conversations between co-workers.

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17

Su, Emily Chia-Yu, Jia-Ming Chang, Cheng-Wei Cheng, Ting-Yi Sung, and Wen-Lian Hsu. "Prediction of nuclear proteins using nuclear translocation signals proposed by probabilistic latent semantic indexing." BMC Bioinformatics 13, S17 (2012). http://dx.doi.org/10.1186/1471-2105-13-s17-s13.

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18

Gupta, Vishal, Dilip Kumar Sharma, and Ashutosh Dixit. "Review of Information Retrieval - Models, Performance Evaluation Techniques and Applications." International Journal of Sensors, Wireless Communications and Control 11 (January 21, 2021). http://dx.doi.org/10.2174/2210327911666210121161142.

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: Information retrieval (IR) is a field that concerns the structure, memory, analysis, and access to pieces of information. It has a wide application in various areas like search engines, communication systems, information filtering, medical search, etc. and helps design efficient and secure applications. This area has been a surge of research from the last few years due to data mining's unparalleled success, deep learning in computer vision, blockchain technology, etc. Core models, performance evaluation techniques, IR system applications, and its role in blockchain technology have been proposed in this literature, calling the need for a broad survey to focus the research in this promising area. This paper fills the space by surveying the state of art approaches with deep learning models, query expansion techniques used, and use of private information retrieval in blockchain technology. This survey paper includes different IR models like boolean model, vector space model, probabilistic model, language model, N-gram model, fuzzy model, Latent Semantic Indexing (LSI) Model, Bayesian network, Evolutionary algorithm based models and Machine Learning based models. Applications of IR systems along with different datasets are also included to provide further research in this field.

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