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Regularized Semi-Supervised Latent Dirichlet Allocation for visual concept learning
2013
Neurocomputing
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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
2017
International Journal of Modern Education and Computer Science
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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
2015
EURASIP Journal on Advances in Signal Processing
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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
2018
EAI Endorsed Transactions on Scalable Information Systems
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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]
2013
Lecture Notes in Computer Science
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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
2009
2009 IEEE International Geoscience and Remote Sensing Symposium
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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
2012
ACM Transactions on Knowledge Discovery from Data
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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
2010
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '10
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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
2013
2013 International Workshop on Pattern Recognition in Neuroimaging
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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
2013
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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
2021
International Journal of Advanced Computer Science and Applications
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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
2009
Image and Vision Computing
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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
2008
2008 IEEE Conference on Computer Vision and Pattern Recognition
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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
2022
Applied Sciences
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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
2013
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
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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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