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Artículos de revistas sobre el tema "Allocation de Dirichlet"

Autor: Grafiati

Publicado: 15 de febrero de 2025

Última modificación: 31 de julio de 2025

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1

Du, Lan, Wray Buntine, Huidong Jin, and Changyou Chen. "Sequential latent Dirichlet allocation." Knowledge and Information Systems 31, no. 3 (2011): 475–503. http://dx.doi.org/10.1007/s10115-011-0425-1.

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2

Schwarz, Carlo. "Ldagibbs: A Command for Topic Modeling in Stata Using Latent Dirichlet Allocation." Stata Journal: Promoting communications on statistics and Stata 18, no. 1 (2018): 101–17. http://dx.doi.org/10.1177/1536867x1801800107.

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In this article, I introduce the ldagibbs command, which implements latent Dirichlet allocation in Stata. Latent Dirichlet allocation is the most popular machine-learning topic model. Topic models automatically cluster text documents into a user-chosen number of topics. Latent Dirichlet allocation represents each document as a probability distribution over topics and represents each topic as a probability distribution over words. Therefore, latent Dirichlet allocation provides a way to analyze the content of large unclassified text data and an alternative to predefined document classifications

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3

Yoshida, Takahiro, Ryohei Hisano, and Takaaki Ohnishi. "Gaussian hierarchical latent Dirichlet allocation: Bringing polysemy back." PLOS ONE 18, no. 7 (2023): e0288274. http://dx.doi.org/10.1371/journal.pone.0288274.

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Topic models are widely used to discover the latent representation of a set of documents. The two canonical models are latent Dirichlet allocation, and Gaussian latent Dirichlet allocation, where the former uses multinomial distributions over words, and the latter uses multivariate Gaussian distributions over pre-trained word embedding vectors as the latent topic representations, respectively. Compared with latent Dirichlet allocation, Gaussian latent Dirichlet allocation is limited in the sense that it does not capture the polysemy of a word such as “bank.” In this paper, we show that Gaussia

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4

Archambeau, Cedric, Balaji Lakshminarayanan, and Guillaume Bouchard. "Latent IBP Compound Dirichlet Allocation." IEEE Transactions on Pattern Analysis and Machine Intelligence 37, no. 2 (2015): 321–33. http://dx.doi.org/10.1109/tpami.2014.2313122.

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5

Pion-Tonachini, Luca, Scott Makeig, and Ken Kreutz-Delgado. "Crowd labeling latent Dirichlet allocation." Knowledge and Information Systems 53, no. 3 (2017): 749–65. http://dx.doi.org/10.1007/s10115-017-1053-1.

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6

S.S., Ramyadharshni, and Pabitha Dr.P. "Topic Categorization on Social Network Using Latent Dirichlet Allocation." Bonfring International Journal of Software Engineering and Soft Computing 8, no. 2 (2018): 16–20. http://dx.doi.org/10.9756/bijsesc.8390.

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7

Li, Gen, and Hazri Jamil. "Teacher professional learning community and interdisciplinary collaborative teaching path under the informationization basic education model." Yugoslav Journal of Operations Research, no. 00 (2024): 29. http://dx.doi.org/10.2298/yjor2403029l.

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The construction of a learning community cannot be separated from the participation of information technology. The current teacher learning community has problems of low interaction efficiency and insufficient enthusiasm for group cooperative teaching. This study adopts the Latent Dirichlet allocation method to process text data generated by teacher interaction from the evolution of knowledge topics in the learning community network space. At the same time, the interaction data of the network community learning space is used to extract the interaction characteristics between teachers, and a co

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8

Syed, Shaheen, and Marco Spruit. "Exploring Symmetrical and Asymmetrical Dirichlet Priors for Latent Dirichlet Allocation." International Journal of Semantic Computing 12, no. 03 (2018): 399–423. http://dx.doi.org/10.1142/s1793351x18400184.

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Latent Dirichlet Allocation (LDA) has gained much attention from researchers and is increasingly being applied to uncover underlying semantic structures from a variety of corpora. However, nearly all researchers use symmetrical Dirichlet priors, often unaware of the underlying practical implications that they bear. This research is the first to explore symmetrical and asymmetrical Dirichlet priors on topic coherence and human topic ranking when uncovering latent semantic structures from scientific research articles. More specifically, we examine the practical effects of several classes of Diri

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9

Garg, Mohit, and Priya Rangra. "Bibliometric Analysis of Latent Dirichlet Allocation." DESIDOC Journal of Library & Information Technology 42, no. 2 (2022): 105–13. http://dx.doi.org/10.14429/djlit.42.2.17307.

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Latent Dirichlet Allocation (LDA) has emerged as an important algorithm in big data analysis that finds the group of topics in the text data. It posits that each text document consists of a group of topics, and each topic is a mixture of words related to it. With the emergence of a plethora of text data, the LDA has become a popular algorithm for topic modeling among researchers from different domains. Therefore, it is essential to understand the trends of LDA researches. Bibliometric techniques are established methods to study the research progress of a topic. In this study, bibliographic dat

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10

Chauhan, Uttam, and Apurva Shah. "Topic Modeling Using Latent Dirichlet allocation." ACM Computing Surveys 54, no. 7 (2022): 1–35. http://dx.doi.org/10.1145/3462478.

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We are not able to deal with a mammoth text corpus without summarizing them into a relatively small subset. A computational tool is extremely needed to understand such a gigantic pool of text. Probabilistic Topic Modeling discovers and explains the enormous collection of documents by reducing them in a topical subspace. In this work, we study the background and advancement of topic modeling techniques. We first introduce the preliminaries of the topic modeling techniques and review its extensions and variations, such as topic modeling over various domains, hierarchical topic modeling, word emb

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11

Guo, Yunyan, and Jianzhong Li. "Distributed Latent Dirichlet Allocation on Streams." ACM Transactions on Knowledge Discovery from Data 16, no. 1 (2021): 1–20. http://dx.doi.org/10.1145/3451528.

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Latent Dirichlet Allocation (LDA) has been widely used for topic modeling, with applications spanning various areas such as natural language processing and information retrieval. While LDA on small and static datasets has been extensively studied, several real-world challenges are posed in practical scenarios where datasets are often huge and are gathered in a streaming fashion. As the state-of-the-art LDA algorithm on streams, Streaming Variational Bayes (SVB) introduced Bayesian updating to provide a streaming procedure. However, the utility of SVB is limited in applications since it ignored

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12

Jonasson, Johan. "Slow mixing for Latent Dirichlet Allocation." Statistics & Probability Letters 129 (October 2017): 96–100. http://dx.doi.org/10.1016/j.spl.2017.05.011.

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13

Tazibt, Ahmed Amir, and Farida Aoughlis. "Latent Dirichlet allocation-based temporal summarization." International Journal of Web Information Systems 15, no. 1 (2019): 83–102. http://dx.doi.org/10.1108/ijwis-04-2018-0023.

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Purpose During crises such as accidents or disasters, an enormous volume of information is generated on the Web. Both people and decision-makers often need to identify relevant and timely content that can help in understanding what happens and take right decisions, as soon it appears online. However, relevant content can be disseminated in document streams. The available information can also contain redundant content published by different sources. Therefore, the need of automatic construction of summaries that aggregate important, non-redundant and non-outdated pieces of information is becomi

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14

Adegoke, M. A., J. O. A. Ayeni, and P. A. Adewole. "Empirical prior latent Dirichlet allocation model." Nigerian Journal of Technology 38, no. 1 (2019): 223. http://dx.doi.org/10.4314/njt.v38i1.27.

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15

Lukins, Stacy K., Nicholas A. Kraft, and Letha H. Etzkorn. "Bug localization using latent Dirichlet allocation." Information and Software Technology 52, no. 9 (2010): 972–90. http://dx.doi.org/10.1016/j.infsof.2010.04.002.

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16

Cahyono, Nuri, Narwanto Nurcahyo, and Akmal Fauzan Restu Agung. "Analisa Perbandingan Latent Semantic Indexing (LSI) dan Latent Dirichlet Allocation (LDA) untuk Topic Modelling Aplikasi Identitas Kependudukan Digital (IKD)." Building of Informatics, Technology and Science (BITS) 6, no. 3 (2024): 1638–47. https://doi.org/10.47065/bits.v6i3.5970.

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This study aims to analyze and compare two topic modeling methods, Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA), in understanding user reviews of the Digital Population Identity (IKD) Application obtained from the Google Play Store. The main problem addressed is the large number of user reviews with diverse topics that are difficult to categorize manually, necessitating an automated method to identify the main themes in the data. The research process began with scraping 5,000 recent reviews, followed by data preprocessing (Remove Punctuation, Lowercase, and Tokenization

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17

Christy, A., Anto Praveena, and Jany Shabu. "A Hybrid Model for Topic Modeling Using Latent Dirichlet Allocation and Feature Selection Method." Journal of Computational and Theoretical Nanoscience 16, no. 8 (2019): 3367–71. http://dx.doi.org/10.1166/jctn.2019.8234.

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In this information age, Knowledge discovery and pattern matching plays a significant role. Topic Modeling, an area of Text mining is used detecting hidden patterns in a document collection. Topic Modeling and Document Clustering are two important key terms which are similar in concepts and functionality. In this paper, topic modeling is carried out using Latent Dirichlet Allocation-Brute Force Method (LDA-BF), Latent Dirichlet Allocation-Back Tracking (LDA-BT), Latent Semantic Indexing (LSI) method and Nonnegative Matrix Factorization (NMF) method. A hybrid model is proposed which uses Latent

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18

ZHANG, Zhifei, Duoqian MIAO, and Can GAO. "Short text classification using latent Dirichlet allocation." Journal of Computer Applications 33, no. 6 (2013): 1587–90. http://dx.doi.org/10.3724/sp.j.1087.2013.01587.

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19

Li, Chenchen, Xiang Yan, Xiaotie Deng, et al. "Latent Dirichlet Allocation for Internet Price War." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 639–46. http://dx.doi.org/10.1609/aaai.v33i01.3301639.

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Current Internet market makers are facing an intense competitive environment, where personalized price reductions or discounted coupons are provided by their peers to attract more customers. Much investment is spent to catch up with each other’s competitors but participants in such a price cut war are often incapable of winning due to their lack of information about others’ strategies or customers’ preference. We formalize the problem as a stochastic game with imperfect and incomplete information and develop a variant of Latent Dirichlet Allocation (LDA) to infer latent variables under the cur

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20

Moss, Fabian C., and Martin Rohrmeier. "Discovering Tonal Profiles with Latent Dirichlet Allocation." Music & Science 4 (January 2021): 205920432110488. http://dx.doi.org/10.1177/20592043211048827.

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Music analysis, in particular harmonic analysis, is concerned with the way pitches are organized in pieces of music, and a range of empirical applications have been developed, for example, for chord recognition or key finding. Naturally, these approaches rely on some operationalization of the concepts they aim to investigate. In this study, we take a complementary approach and discover latent tonal structures in an unsupervised manner. We use the topic model Latent Dirichlet Allocation and apply it to a large historical corpus of musical pieces from the Western classical tradition. This method

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21

Ohmura, Masahiro, Koh Kakusho, and Takeshi Okadome. "Tweet Sentiment Analysis with Latent Dirichlet Allocation." International Journal of Information Retrieval Research 4, no. 3 (2014): 66–79. http://dx.doi.org/10.4018/ijirr.2014070105.

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The method proposed here analyzes the social sentiments from collected tweets that have at least 1 of 800 sentimental or emotional adjectives. By dealing with tweets posted in a half a day as an input document, the method uses Latent Dirichlet Allocation (LDA) to extract social sentiments, some of which coincide with our daily sentiments. The extracted sentiments, however, indicate lowered sensitivity to changes in time, which suggests that they are not suitable for predicting daily social or economic events. Using LDA for the representative 72 adjectives to which each of the 800 adjectives ma

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22

Rasiwasia, N., and N. Vasconcelos. "Latent Dirichlet Allocation Models for Image Classification." IEEE Transactions on Pattern Analysis and Machine Intelligence 35, no. 11 (2013): 2665–79. http://dx.doi.org/10.1109/tpami.2013.69.

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23

Liu, Hailin, Ling Xu, Mengning Yang, Meng Yan, and Xiaohong Zhang. "Predicting Component Failures Using Latent Dirichlet Allocation." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/562716.

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Latent Dirichlet Allocation (LDA) is a statistical topic model that has been widely used to abstract semantic information from software source code. Failure refers to an observable error in the program behavior. This work investigates whether semantic information and failures recorded in the history can be used to predict component failures. We use LDA to abstract topics from source code and a new metric (topic failure density) is proposed by mapping failures to these topics. Exploring the basic information of topics from neighboring versions of a system, we obtain a similarity matrix. Multipl

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24

Pan, Lili, Shen Cheng, Jian Liu, et al. "Latent Dirichlet allocation based generative adversarial networks." Neural Networks 132 (December 2020): 461–76. http://dx.doi.org/10.1016/j.neunet.2020.08.012.

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25

Xia, Wei, and Hani Doss. "Scalable Hyperparameter Selection for Latent Dirichlet Allocation." Journal of Computational and Graphical Statistics 29, no. 4 (2020): 875–95. http://dx.doi.org/10.1080/10618600.2020.1741378.

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26

Li, Zhoujun, Haijun Zhang, Senzhang Wang, Feiran Huang, Zhenping Li, and Jianshe Zhou. "Exploit latent Dirichlet allocation for collaborative filtering." Frontiers of Computer Science 12, no. 3 (2018): 571–81. http://dx.doi.org/10.1007/s11704-016-6078-1.

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27

Anandkumar, Anima, Dean P. Foster, Daniel Hsu, Sham M. Kakade, and Yi-Kai Liu. "A Spectral Algorithm for Latent Dirichlet Allocation." Algorithmica 72, no. 1 (2014): 193–214. http://dx.doi.org/10.1007/s00453-014-9909-1.

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28

Yao, Jiangchao, Yanfeng Wang, Ya Zhang, Jun Sun, and Jun Zhou. "Joint Latent Dirichlet Allocation for Social Tags." IEEE Transactions on Multimedia 20, no. 1 (2018): 224–37. http://dx.doi.org/10.1109/tmm.2017.2716829.

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29

Biggers, Lauren R., Cecylia Bocovich, Riley Capshaw, Brian P. Eddy, Letha H. Etzkorn, and Nicholas A. Kraft. "Configuring latent Dirichlet allocation based feature location." Empirical Software Engineering 19, no. 3 (2012): 465–500. http://dx.doi.org/10.1007/s10664-012-9224-x.

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30

Kim, Anastasiia, Sanna Sevanto, Eric R. Moore, and Nicholas Lubbers. "Latent Dirichlet Allocation modeling of environmental microbiomes." PLOS Computational Biology 19, no. 6 (2023): e1011075. http://dx.doi.org/10.1371/journal.pcbi.1011075.

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Interactions between stressed organisms and their microbiome environments may provide new routes for understanding and controlling biological systems. However, microbiomes are a form of high-dimensional data, with thousands of taxa present in any given sample, which makes untangling the interaction between an organism and its microbial environment a challenge. Here we apply Latent Dirichlet Allocation (LDA), a technique for language modeling, which decomposes the microbial communities into a set of topics (non-mutually-exclusive sub-communities) that compactly represent the distribution of ful

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31

Choi, Taeheon, Sangin Park, and Joon Soon Kim. "Trend Analysis on Korean Forest Trails Using LDA(Latent Dirichlet Allocation) Topic Modeling." Korean Journal of Forest Economic 30, no. 1 (2023): 27–38. https://doi.org/10.31541/kjfe.30.1.3.

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32

Damane, Moeti. "Topic Classification of Central Bank Monetary Policy Statements: Evidence from Latent Dirichlet Allocation in Lesotho." Acta Universitatis Sapientiae, Economics and Business 10, no. 1 (2022): 199–227. http://dx.doi.org/10.2478/auseb-2022-0012.

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Abstract This article develops a baseline on how to analyse the statements of monetary policy from Lesotho’s Central Bank using a method of topic classification that utilizes a machine learning algorithm known as Latent Dirichlet Allocation. To evaluate the changes in the policy distribution, the classification of topics is performed on a sample of policy statements spanning from February 2017 to January 2021. The three-topic Latent Dirichlet Allocation model extracted topics that remained prominent throughout the sample period and were most closely reflective of the functions of the Central B

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33

Fatima-Zahrae, Sifi, Sabbar Wafae, and El Mzabi Amal. "Application of Latent Dirichlet Allocation (LDA) for clustering financial tweets." E3S Web of Conferences 297 (2021): 01071. http://dx.doi.org/10.1051/e3sconf/202129701071.

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Sentiment classification is one of the hottest research areas among the Natural Language Processing (NLP) topics. While it aims to detect sentiment polarity and classification of the given opinion, requires a large number of aspect extractions. However, extracting aspect takes human effort and long time. To reduce this, Latent Dirichlet Allocation (LDA) method have come out recently to deal with this issue.In this paper, an efficient preprocessing method for sentiment classification is presented and will be used for analyzing user’s comments on Twitter social network. For this purpose, differe

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34

Suparyati, Suparyati, and Emma Utami. "Pengamatan Tren Ulasan Hotel Menggunakan Pemodelan Topik Berbasis Latent Dirichlet Allocation." JIKO (Jurnal Informatika dan Komputer) 6, no. 2 (2022): 169. http://dx.doi.org/10.26798/jiko.v6i2.579.

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Ketepatan dalam mengekstrak dan meringkas ribuan ulasan ke dalam beberapa topik menjadi kunci dalam pelaksanaan pengolahan data dan informasi lebih lanjut. Tidak terkecuali dalam industri perhotelan yang mana suatu ulasan merupakan sebuah aset yang apabila diolah dapat menghasilkan suatu informasi yang nantinya akan digunakan untuk kepentingan ekspansi bisnis dan keberlangsungan usahanya. Penelitian pemodelan topik ulasan hotel ini menggunakan Latent Dirichlet Allocation sebagai sarana untuk peringkasan dokumennya. Latent Dirichlet Allocation terbukti efektif dalam pengolahan peringkasan kata-

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35

Duraivel, Samuel, Lavanya Lavanya, and Aby Augustine. "Understanding Vaccine Hesitancy with Application of Latent Dirichlet Allocation to Reddit Corpora." Indian Journal Of Science And Technology 15, no. 37 (2022): 1868–75. http://dx.doi.org/10.17485/ijst/v15i37.687.

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36

Rakhmawati, Nur Aini, Rekyan Bayu Waskitho, Dimas Arief Rahman, and Muhammad Fajrul Alam Ulin Nuha. "Klasterisasi Topik Konten Channel Youtube Gaming Indonesia Menggunakan Latent Dirichlet Allocation." Journal of Information Engineering and Educational Technology 5, no. 2 (2021): 78–83. http://dx.doi.org/10.26740/jieet.v5n2.p78-83.

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Youtube adalah platform untuk saling berbagi video terbesar di internet. Semakin platform ini berkembangan, semakin banyak konten yang tersedia di dalamnya, yang dikarenakan semakin beragam genre videonya. Salah satu genre video yang sedang naik daun adalah konten gaming, yang mana topik tersebut adalah objek pada penelitian ini. Penelitian ini dilakukan dengan menggunakan metode Latent Dirichlect Allocation (LDA) untuk memetakan topik-topik dari genre gaming ini. Data didapatkan dari 10 kanal gaming dengan subscribers terbanyak di Indonesia, yang diekstrak dengan melakukan text mining. Total

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37

Kozlowski, Diego, Viktoriya Semeshenko, and Andrea Molinari. "Latent Dirichlet allocation model for world trade analysis." PLOS ONE 16, no. 2 (2021): e0245393. http://dx.doi.org/10.1371/journal.pone.0245393.

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International trade is one of the classic areas of study in economics. Its empirical analysis is a complex problem, given the amount of products, countries and years. Nowadays, given the availability of data, the tools used for the analysis can be complemented and enriched with new methodologies and techniques that go beyond the traditional approach. This new possibility opens a research gap, as new, data-driven, ways of understanding international trade, can help our understanding of the underlying phenomena. The present paper shows the application of the Latent Dirichlet allocation model, a

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38

Zhou, Qi, Haipeng Chen, Yitao Zheng, and Zhen Wang. "EvaLDA: Efficient Evasion Attacks Towards Latent Dirichlet Allocation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14602–11. http://dx.doi.org/10.1609/aaai.v35i16.17716.

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As one of the most powerful topic models, Latent Dirichlet Allocation (LDA) has been used in a vast range of tasks, including document understanding, information retrieval and peer-reviewer assignment. Despite its tremendous popularity, the security of LDA has rarely been studied. This poses severe risks to security-critical tasks such as sentiment analysis and peer-reviewer assignment that are based on LDA. In this paper, we are interested in knowing whether LDA models are vulnerable to adversarial perturbations of benign document examples during inference time. We formalize the evasion attac

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39

Fernanda, Jerhi Wahyu. "PEMODELAN PERSEPSI PEMBELAJARAN ONLINE MENGGUNAKAN LATENT DIRICHLET ALLOCATION." Jurnal Statistika Universitas Muhammadiyah Semarang 9, no. 2 (2021): 79. http://dx.doi.org/10.26714/jsunimus.9.2.2021.79-85.

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Latent Dirichlet Allocation (LDA) merupakan metode untuk pemodelan topik adalah yang didasarkan kepada konsep probabilitas untuk mencari kemiripan suatu dokumen dan mengelompokkan dokumen-dokumen menjadi beberapa topik atau kelompok. Metode ini masuk dalam unsupervised learning karena tidak ada label atau target pada data yang dianalisis. Penelitian ini bertujuan untuk mengelompokkan persepsi tentang pembelajaran online ke dalam beberapa topik menggunakan metode LDA. Data penelitian ini adalah data primer yang dikumpulkan melalui formulir online. Hasil analisis menunjukkan bahwa pemodelan LDA

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40

Junwei Zeng, Fajie Wei, and Anying Liu. "Employing Latent Dirichlet Allocation for Organizational Risk Identification." Journal of Convergence Information Technology 6, no. 12 (2011): 114–21. http://dx.doi.org/10.4156/jcit.vol6.issue12.15.

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41

Hong, Fan, Chufan Lai, Hanqi Guo, Enya Shen, Xiaoru Yuan, and Sikun Li. "FLDA: Latent Dirichlet Allocation Based Unsteady Flow Analysis." IEEE Transactions on Visualization and Computer Graphics 20, no. 12 (2014): 2545–54. http://dx.doi.org/10.1109/tvcg.2014.2346416.

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42

Ihler, A., and D. Newman. "Understanding Errors in Approximate Distributed Latent Dirichlet Allocation." IEEE Transactions on Knowledge and Data Engineering 24, no. 5 (2012): 952–60. http://dx.doi.org/10.1109/tkde.2011.29.

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43

Chen, Chaotao, and Jiangtao Ren. "Forum latent Dirichlet allocation for user interest discovery." Knowledge-Based Systems 126 (June 2017): 1–7. http://dx.doi.org/10.1016/j.knosys.2017.04.006.

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44

Bhutada, Sunil, V. V. S. S. S. Balaram, and Vishnu Vardhan Bulusu. "Semantic latent dirichlet allocation for automatic topic extraction." Journal of Information and Optimization Sciences 37, no. 3 (2016): 449–69. http://dx.doi.org/10.1080/02522667.2016.1165000.

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45

Yan, Jian-Feng, Jia Zeng, Yang Gao, and Zhi-Qiang Liu. "Communication-efficient algorithms for parallel latent Dirichlet allocation." Soft Computing 19, no. 1 (2014): 3–11. http://dx.doi.org/10.1007/s00500-014-1376-8.

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46

Jeong, Young-Seob, and Ho-Jin Choi. "Overlapped latent Dirichlet allocation for efficient image segmentation." Soft Computing 19, no. 4 (2014): 829–38. http://dx.doi.org/10.1007/s00500-014-1410-x.

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47

Wang, Jingdong, Jiazhen Zhou, Hao Xu, Tao Mei, Xian-Sheng Hua, and Shipeng Li. "Image tag refinement by regularized latent Dirichlet allocation." Computer Vision and Image Understanding 124 (July 2014): 61–70. http://dx.doi.org/10.1016/j.cviu.2014.02.011.

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48

Zhang, Wei, Robert A. J. Clark, Yongyuan Wang, and Wen Li. "Unsupervised language identification based on Latent Dirichlet Allocation." Computer Speech & Language 39 (September 2016): 47–66. http://dx.doi.org/10.1016/j.csl.2016.02.001.

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49

Momtazi, Saeedeh. "Unsupervised Latent Dirichlet Allocation for supervised question classification." Information Processing & Management 54, no. 3 (2018): 380–93. http://dx.doi.org/10.1016/j.ipm.2018.01.001.

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Zhao, Fangyuan, Xuebin Ren, Shusen Yang, Qing Han, Peng Zhao, and Xinyu Yang. "Latent Dirichlet Allocation Model Training With Differential Privacy." IEEE Transactions on Information Forensics and Security 16 (2021): 1290–305. http://dx.doi.org/10.1109/tifs.2020.3032021.

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