Results 251 to 260 of about 118,657 (309)

Dual LDA for Face Recognition

Fundamenta Informaticae, 2004
The complete theory for Fisher and dual discriminant analysis is presented as the background of the novel algorithms. LDA is found as composition of projection onto the singular subspace for within-class normalised data with the projection onto the singular subspace for between-class normalised data.
Skarbek, W, Kucharski, K, Bober, M
openaire   +4 more sources

Content analysis of e-petitions with topic modeling: How to train and evaluate LDA models?

Information Processing & Management, 2018
E-petitions have become a popular vehicle for political activism, but studying them has been difficult because efficient methods for analyzing their content are currently lacking.
Loni Hagen
semanticscholar   +1 more source

ADM-LDA

Journal of Information Science, 2014
Probabilistic topic models are statistical methods whose aim is to discover the latent structure in a large collection of documents. The intuition behind topic models is that, by generating documents by latent topics, the word distribution for each topic can be modelled and the prior distribution over the topic learned.
Bagheri, Ayoub, Saraee, Mohamad, De Jong, Franciska   +2 more
openaire   +1 more source

Performance of LDA and DCT models

Journal of Information Science, 2014
The Doubly Correlated Topic Model is a generative probabilistic topic model for automatically identifying topics from the corpus of the text documents. It is a mixed membership model, based on the fact that a document exhibits a number of topics. We used word co-occurrence statistical information for identifying an initial set of topics as posterior ...
Abhishek Singh Rathore, Devshri Roy
openaire   +1 more source

Tag refinement by regularized LDA

Proceedings of the 17th ACM international conference on Multimedia, 2009
Tagging is nowadays the most prevalent and practical way to make images searchable. However, in reality many tags are irrelevant to image content. To refine the tags, previous solutions usually mine tag relevance relying on the tag similarity estimated right from the corpus to be refined.
Hao Xu, Jingdong Wang 0001, Xian-Sheng Hua 0001, Shipeng Li 0001   +3 more
openaire   +1 more source

On an equivalence between PLSI and LDA

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, 2003
Latent Dirichlet Allocation (LDA) is a fully generative approach to language modelling which overcomes the inconsistent generative semantics of Probabilistic Latent Semantic Indexing (PLSI). This paper shows that PLSI is a maximum a posteriori estimated LDA model under a uniform Dirichlet prior, therefore the perceived shortcomings of PLSI can be ...
Mark A. Girolami, Ata Kabán
openaire   +1 more source

LDA*

Proceedings of the VLDB Endowment, 2017
We present LDA*, a system that has been deployed in one of the largest Internet companies to fulfil their requirements of "topic modeling as an internal service" ---relying on thousands of machines, engineers in different sectors submit their data, some are as large as 1.8TB, to LDA* and get results back in hours ...
Lele Yu, Bin Cui 0001, Ce Zhang 0001, Yingxia Shao   +3 more
openaire   +1 more source

A Comparative Study of PCA, LDA and Kernel LDA for Image Classification

2009 International Symposium on Ubiquitous Virtual Reality, 2009
Although various discriminant analysis approaches have been used in Content-Based Image Retrieval (CBIR) application, there have been relatively few concerns with kernel-based methods. Furthermore, these CBIR applications still applied discriminant analysis to face images as face recognition did.
Fei Ye, Zhiping Shi, Zhongzhi Shi
openaire   +1 more source

Parallel LDA with Over-Decomposition

2017 IEEE 24th International Conference on High Performance Computing Workshops (HiPCW), 2017
Latent Dirichlet Allocation (LDA) is a statistical technique for topic modeling. Prior efforts to parallelize LDA have either used expensive atomic operations or weakened the sampling model to enable parallelization without heavy use of atomics. In this paper, we present a parallel LDA implementation that uses an over-decomposed 2D tiling strategy to ...
Gordon Euhyun Moon, Aravind Sukumaran-Rajam, P. Sadayappan   +2 more
openaire   +2 more sources

Discovering topics and trends in the field of Artificial Intelligence: Using LDA topic modeling

Expert systems with applications, 2023
Dejian Yu, Bo Xiang
semanticscholar   +1 more source