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What is LDA

Handbook of Research on Computational Forensics, Digital Crime, and Investigation: Methods and Solutions
LDA stands for Linear Discrimant Analysis. Also known as Fisher Discriminant Analysis (FDA) or Fisher Linear Discrimant (FLD). This is popular in face recognition method of dimensionality reduction that search for vectors that best discriminate among classes.
Published in Chapter:
Biometrical Processing of Faces in Security and Forensics
Pawel T. Puslecki (National University of Ireland, Ireland)
DOI: 10.4018/978-1-60566-836-9.ch004
The aim of this chapter is the overall and comprehensive description of the machine face processing issue and presentation of its usefulness in security and forensic applications. The chapter overviews the methods of face processing as the field deriving from various disciplines. After a brief introduction to the field, the conclusions concerning human processing of faces that have been drawn by the psychology researchers and neuroscientists are described. Then the most important tasks related to the computer facial processing are shown: face detection, face recognition and processing of facial features, and the main strategies as well as the methods applied in the related fields are presented. Finally, the applications of digital biometrical processing of human faces are presented.
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More Results
An Interactive Personalized Spatial Keyword Querying Approach
LDA is a kind of unsupervised machine learning technology, which can be used to identify the hidden subject information in massive document collection or corpus.
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Automated Image Analysis Approaches in Histopathology
Linear Discriminant Analysis. A dimension reduction technique used to transform a feature set into a smaller set of features that best discriminates between the different classes in the data.
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Feature Extraction Techniques: Fundamental Concepts and Survey
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Effective Entity Linking and Disambiguation Algorithms for User-Generated Content (UGC)
In natural language processing, latent dirichlet allocation is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.
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