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Regularized Semi-Supervised Latent Dirichlet Allocation for visual concept learning

Liansheng Zhuang, Haoyuan Gao, Jiebo Luo, Zhouchen Lin
2013 Neurocomputing  
In this paper, to take advantage of both limited labeled training images and rich unlabeled images, we propose a novel regularized Semi-Supervised Latent Dirichlet Allocation (r-SSLDA) for learning visual  ...  Topic model is a popular tool for visual concept learning. Most topic models are either unsupervised or fully supervised.  ...  Yi Ma (Microsoft Research Asia) for his helpful conversations about sparse representation and low rank representation. We also thank anonymous reviewers for their constructive comments.  ... 
doi:10.1016/j.neucom.2012.04.043 fatcat:skkisypofbfohaa5jplws657bq

A Survey on Journey of Topic Modeling Techniques from SVD to Deep Learning

Deepak Sharma, Bijendra Kumar, Satish Chand
2017 International Journal of Modern Education and Computer Science  
Here we present a survey on journey of topic modeling techniques comprising Latent Dirichlet Allocation (LDA) and non-LDA based techniques and the reason for classify the techniques into LDA and non-LDA  ...  learning for our survey.  ...  Most of the topics modeling techniques are fully unsupervised; there are few models used semi-supervised or supervised learning (S) for classify topics based on labeled data.  ... 
doi:10.5815/ijmecs.2017.07.06 fatcat:nadnmsoj4zdi7onlxivrne6gqm

Class-specific Gaussian-multinomial latent Dirichlet allocation for image annotation

Zhiming Qian, Ping Zhong, Runsheng Wang
2015 EURASIP Journal on Advances in Signal Processing  
To bridge the semantic gap, we present an extension of latent Dirichlet allocation (LDA), denoted as class-specific Gaussian-multinomial latent Dirichlet allocation (csGM-LDA), in an effort to simulate  ...  To address this, csGM-LDA is introduced by using class supervision at the level of visual features for multimodal topic modeling.  ...  Taking advantage of limited tagged training images and rich untagged images, the work in [31] proposed a regularized semi-supervised latent Dirichlet allocation (r-SSLDA) for learning visual concept  ... 
doi:10.1186/s13634-015-0224-z fatcat:jyy76ste6zc6xlgaunvysya2bq

Topic Modeling: A Comprehensive Review

Pooja Kherwa, Poonam Bansal
2018 EAI Endorsed Transactions on Scalable Information Systems  
It includes classification hierarchy, Topic modelling methods, Posterior Inference techniques, different evolution models of latent Dirichlet allocation (LDA) and its applications in different areas of  ...  Quantitative evaluation of topic modeling techniques is also presented in detail for better understanding the concept of topic modeling.  ...  with semi Supervised fashion in a very limited domains of Application.  ... 
doi:10.4108/eai.13-7-2018.159623 fatcat:lu6al57vp5aahbytyejhqrlzry

From Topic Models to Semi-supervised Learning: Biasing Mixed-Membership Models to Exploit Topic-Indicative Features in Entity Clustering [chapter]

Ramnath Balasubramanyan, Bhavana Dalvi, William W. Cohen
2013 Lecture Notes in Computer Science  
This ability allows us to span the range from unsupervised topic models to semi-supervised learning in the same mixed membership model.  ...  We present methods to introduce different forms of supervision into mixed-membership latent variable models.  ...  We then presented a method to allow for stronger supervision in the form of feature and document labels to move further along the spectrum toward semi-supervised learning from totally unsupervised learning  ... 
doi:10.1007/978-3-642-40991-2_40 fatcat:zkfkornyw5fu3oham5raddpwca

Image semantic coding using OTB

Marie Lienou, Marine Campedel
2009 2009 IEEE International Geoscience and Remote Sensing Symposium  
This process is based on a basic decomposition of the image content into "visual words"; using spatial relationships, these visual words are aggregated to form structures and (inter)active learning is  ...  A complete OTB tool has been developed to illustrate the whole process; it allows feature extraction and "visual words" production, as well as LDA and SVM learning approachs.  ...  Actually, generative probabilistic models such as probabilistic Latent Semantic Analysis (pLSA) [1] and Latent Dirichlet Allocation (LDA) [2] have been exploited for this purpose.  ... 
doi:10.1109/igarss.2009.5417484 dblp:conf/igarss/LienouC09 fatcat:u2cfuwhsmngp5doade5hhcer6u

Discriminative Topic Modeling Based on Manifold Learning

Seungil Huh, Stephen E. Fienberg
2012 ACM Transactions on Knowledge Discovery from Data  
Previous topic models, such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA), have shown impressive success in discovering low-rank hidden structures for modeling  ...  As a result, DTM achieves higher classification performance in a semi-supervised setting by effectively exposing the manifold structure of data.  ...  We thank the anonymous reviewers for their insightful feedback and Yoongu Kim for providing helpful comments for the final manuscript.  ... 
doi:10.1145/2086737.2086740 fatcat:6vwksi73sbeenajjmrsacbtuli

Discriminative topic modeling based on manifold learning

Seungil Huh, Stephen E. Fienberg
2010 Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '10  
Previous topic models, such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA), have shown impressive success in discovering low-rank hidden structures for modeling  ...  As a result, DTM achieves higher classification performance in a semi-supervised setting by effectively exposing the manifold structure of data.  ...  We thank the anonymous reviewers for their insightful feedback and Yoongu Kim for providing helpful comments for the final manuscript.  ... 
doi:10.1145/1835804.1835888 dblp:conf/kdd/HuhF10 fatcat:2tt5cmr34rczrem3utw5ilpava

Learning Predictive Cognitive Structure from fMRI Using Supervised Topic Models

Oluwasanmi Koyejo, Priyank Patel, Joydeep Ghosh, Russell A. Poldrack
2013 2013 International Workshop on Pattern Recognition in Neuroimaging  
Our results motivate the use of supervised topic models for analyzing cognitive function with fMRI.  ...  We represent the images as documents and the mental concepts as topics, and evaluate the effectiveness of unsupervised topic models for the recovery of the task to mental concept mapping, We also evaluate  ...  We present experimental results evaluating the following models: 1) Latent Dirichlet allocation (LDA) [4] : is a popular model for topic modeling.  ... 
doi:10.1109/prni.2013.12 dblp:conf/prni/KoyejoPGP13 fatcat:ypxmtggmrvhxhpq6xskfbmniy4

Latent Dirichlet Allocation Models for Image Classification

N. Rasiwasia, N. Vasconcelos
2013 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Two new extensions of latent Dirichlet allocation (LDA), denoted topic-supervised LDA (ts-LDA) and class-specific-simplex LDA (css-LDA), are proposed for image classification.  ...  This implies that the discovered topics are driven by general image regularities, rather than the semantic regularities of interest for classification.  ...  CLASS-SPECIFIC-SIMPLEX LATENT DIRICHLET ALLOCATION To overcome this limitation, we introduce a new LDA model for image classification, denoted css-LDA.  ... 
doi:10.1109/tpami.2013.69 pmid:24051727 fatcat:ofireem56jegvjfxok37qhbxqe

A Hybrid Model for Documents Representation

Dina Mohamed, Ayman El-Kilany, Hoda M.
2021 International Journal of Advanced Computer Science and Applications  
The proposed model aims to learn a vector for each document using the relationship between its words' vectors and the hierarchy of topics generated using the hierarchical Latent Dirichlet Allocation model  ...  In this paper, we aim to build a model to represent text semantically either in one document or multiple documents using a combination of hierarchical Latent Dirichlet Allocation (hLDA), Word2vec, and  ...  Latent Dirichlet Allocation (LDA) [6] is one of the main topic modeling methods.  ... 
doi:10.14569/ijacsa.2021.0120339 fatcat:pibiib6r6rgenkfv5mzen2ukca

Latent mixture vocabularies for object categorization and segmentation

Diane Larlus, Frédéric Jurie
2009 Image and Vision Computing  
The visual vocabulary is an intermediate level representation which has been proved to be very powerful for addressing object categorization problems.  ...  We propose here to embed the visual vocabulary creation within the object model construction, allowing to make it more suited for object class discrimination and therefore for object categorization.  ...  The bag-of-features strategy inspired more complex models, like probabilistic Latent Semantic Analysis (pLSA) [12] , or its Bayesian form Latent Dirichlet Allocation (LDA) [2] .  ... 
doi:10.1016/j.imavis.2008.04.022 fatcat:baccufur7vdmtiexdkkf6exy3q

Unsupervised discovery of visual object class hierarchies

Josef Sivic, Bryan C. Russell, Andrew Zisserman, William T. Freeman, Alexei A. Efros
2008 2008 IEEE Conference on Computer Vision and Pattern Recognition  
This is achieved by adapting to the visual domain the generative Hierarchical Latent Dirichlet Allocation (hLDA) model previously used for unsupervised discovery of topic hierarchies in text.  ...  image collections without supervision.  ...  LDA: The Latent Dirichlet Allocation model assumes that documents are generated from a set of K latent topics.  ... 
doi:10.1109/cvpr.2008.4587622 dblp:conf/cvpr/SivicRZFE08 fatcat:hbwnebsxmfbv3ohazsjhjmotu4

A Survey of Data Representation for Multi-Modality Event Detection and Evolution

Kejing Xiao, Zhaopeng Qian, Biao Qin
2022 Applied Sciences  
Next, we discuss the techniques of data representation for event detection, including textual, visual, and multi-modality content. Finally, we review event evolution under multi-modality data.  ...  The rapid growth of online data has made it very convenient for people to obtain information. However, it also leads to the problem of "information overload".  ...  [96] proposed a boosted multimodal supervised Latent Dirichlet Allocation (BMM-SLDA), which is used for the event classification task.  ... 
doi:10.3390/app12042204 fatcat:5gpezz6yhjejlmdzr5fhpgka6m

Visual pattern discovery in image and video data: a brief survey

Hongxing Wang, Gangqiang Zhao, Junsong Yuan
2013 Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery  
At the end we identify the open issues for future research.  ...  In image and video data, visual pattern refers to re-occurring composition of visual primitives. Such visual patterns extract the essence of the image and video data that convey rich information.  ...  To engage human in the loop for video object discovery, Liu et al. 53 employ the topic model in a semi-supervised learning framework.  ... 
doi:10.1002/widm.1110 fatcat:skjnmv5njfdtxc3erl4r2txqri
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