FLDAFunctional Linear Discriminant Analysis
FLDAFisher’s Linear Discriminant Analysis
FLDAFantasy League Dart Association
FLDAFuzzy Logic Decision Algorithm
FLDAFuzzy Linear Discriminant Analysis
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References in periodicals archive ?
2012 [10] FLDA Own (KSA dataset) Our work ANN KSA dataset Author Acc (%) Sheikhani et al.
FLDA extends the Link-LDA model by capturing the content-related and content-independent reasons why a user follows another on microblog.
In the following, we compare Union model with TwitterRank and FLDA. The TwitterRank algorithm was originally proposed to find topic-level authorities on Twitter.
Compared with FLDA method and DLDA method, experimental results show that the proposed GLDA method outperforms FLDA and DLDA methods.
In the pattern recognition community, Fisher Linear Discriminant Analysis (FLDA) [18] is one of the most used analytical tools to transform the raw data into a lower dimensional subspace by maximizing a class separation criterion.
Until now, various feature extraction methods have been explored, including classical subspace-based dimension reduction approaches like principal component analysis (PCA), fisher linear discriminant analysis (FLDA), independent component analysis (ICA), and so on [6].
Fisher's linear discriminant analysis (FLDA) tries to find a projection matrix that projects the training data onto a low-dimensional space that maximizes between-class variance as well as minimizing within-class variance [48, 56].
Face recognition using FLDA with single training image per person, Elsevier, Applied Mathematics and Computation, 205(2): 726-734.
[8] Quan-xue Gao, Lei Zhang, and David Zhang, "Face recognition using FLDA with single training image per person," Applied Mathematics and Computation, vol.
For classification, we used a Fisher Linear Discriminant Analysis (FLDA) as our classifier.
The performance of S2DPCA is compared with 2DPCA, PCA and FLDA in terms of accuracy.