Results 241 to 250 of about 306,626 (287)

PCA versus LDA

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001
In the context of the appearance-based paradigm for object recognition, it is generally believed that algorithms based on LDA (linear discriminant analysis) are superior to those based on PCA (principal components analysis). In this communication, we show that this is not always the case.
Avinash C. Kak, Aleix M. Martinez
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Optimization of LDA parameters

2020 28th Signal Processing and Communications Applications Conference (SIU), 2020
The aim of topic modeling is to automatically discover topics in large collections of documents. Although it is used in many different fields, the questions of how to eliminate topic instability and how to optimize model parameters are not fully answered yet.
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Learning Regularized LDA by Clustering

IEEE Transactions on Neural Networks and Learning Systems, 2014
As a supervised dimensionality reduction technique, linear discriminant analysis has a serious overfitting problem when the number of training samples per class is small. The main reason is that the between- and within-class scatter matrices computed from the limited number of training samples deviate greatly from the underlying ones.
Pang, Yanwei, Wang, Shuang, Yuan, Yuan
openaire   +4 more sources

LDA-based document models for ad-hoc retrieval

Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006
Search algorithms incorporating some form of topic model have a long history in information retrieval. For example, cluster-based retrieval has been studied since the 60s and has recently produced good results in the language model framework. An approach
Xing Wei, W. Bruce Croft
semanticscholar   +1 more source

Exchange interactions in (ZnMn)Se: LDA and LDA+U calculations

Physical Review B, 2003
One of the remarkable properties of the II-VI diluted magnetic semiconductor (ZnMn)Se is the giant spin splitting of the valence-band states under application of the magnetic field (giant Zeeman splitting). This splitting reveals strong exchange interaction between Mn moments and semiconductor states. On the other hand, no magnetic phase transition has
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Deep LDA : A new way to topic model

Journal of Information and Optimization Sciences, 2019
Probabilistic topic models like Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA) and Biterm Topic Model (BTM) have been successfully implemented and used in many areas like movie reviews, recommender systems and text summarization etc ...
M. Bhat, Majid Kundroo, T. A. Tarray, Basant Agarwal   +3 more
semanticscholar   +1 more source

WT-LDA: User Tagging Augmented LDA for Web Service Clustering [PDF]

, 2013
Clustering Web services that groups together services with similar functionalities helps improve both the accuracy and efficiency of the Web service search engines. An important limitation of existing Web service clustering approaches is that they solely focus on utilizing WSDL Web Service Description Language documents.
Yilun Wang, Jian Wu, Zibin Zheng, Liang Chen, Qi Yu   +4 more
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Linear discriminant analysis (LDA)

2021
Discriminant analysis (DA) is a descriptive multivariate technique for analyzing grouped data, i.e. the rows of the data matrix are divided into a number of groups that usually represent samples from different populations (Krzanowski, 2003; McLachlan, 2004; Seber, 2004). Recently DA has also been viewed as a promising dimensionality reduction technique
Nickolay Trendafilov, Michele Gallo
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A Non-Greedy Algorithm for L1-Norm LDA

IEEE Transactions on Image Processing, 2017
Recently, L1-norm-based discriminant subspace learning has attracted much more attention in dimensionality reduction and machine learning. However, most existing approaches solve the column vectors of the optimal projection matrix one by one with greedy ...
Yang Liu, Quanxue Gao, Shuo Miao, Xinbo Gao, F. Nie, Yunsong Li   +5 more
semanticscholar   +1 more source

1D-LDA verses 2D-LDA in online handwriting recognition

International Conference on Circuits, Communication, Control and Computing, 2014
The paper compares the performance of both one-dimensional (ID) and two-dimensional (2D) linear discriminant analysis (LDA) in recognizing online handwritten Kannada characters. The main difference between 1D-LDA and 2D-LDA is the way the data is presented to these tools for dimensionality reduction.
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