Hierarchical Topic Modeling Based on the Combination of Formal Concept Analysis and Singular Value Decomposition

2016 ◽  
pp. 357-368 ◽  
Author(s):  
Miroslav Smatana ◽  
Peter Butka
Keyword(s):  
Singular Value Decomposition ◽  
Formal Concept Analysis ◽  
Topic Modeling ◽  
Concept Analysis ◽  
Singular Value ◽  
Formal Concept ◽  
Value Decomposition

MINING ASSOCIATIONS IN HEALTH CARE DATA USING FORMAL CONCEPT ANALYSIS AND SINGULAR VALUE DECOMPOSITION

2010 ◽  
Vol 18 (04) ◽  
pp. 787-807 ◽  
Author(s):  
CH. ASWANI KUMAR ◽  
S. SRINIVAS
Keyword(s):  
Health Care ◽  
Singular Value Decomposition ◽  
Formal Concept Analysis ◽  
Concept Analysis ◽  
Singular Value ◽  
High Dimensionality ◽  
Formal Context ◽  
Formal Concept ◽  
Value Decomposition ◽  
Better Than

In recent times Formal Concept Analysis (FCA), in which the data is represented as a formal context, has gained popularity for Association Rules Mining (ARM). Application of ARM in health care datasets is challenging and a highly rewarding problem. However, datasets in the medical domain are of high dimension. As the dimensionality of dataset increases, size of the formal context as well as complexity of FCA based ARM also increases. To handle the problem of high dimensionality and mine the associations, we propose to apply Singular Value Decomposition (SVD) on the dataset to reduce the dimensionality and apply FCA on the reduced dataset for ARM. To demonstrate the proposed method, experiments are conducted on Tuberculosis (TB) and Hypertension (HP) datasets. Results indicate that with fewer concepts, SVD based FCA has achieved the performance of FCA on TB data and performed better than FCA on HP data.

scholarly journals The Singular Value Decomposition over Completed Idempotent Semifields

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1577
Author(s):  
Francisco Valverde-Albacete ◽  
Carmen Peláez-Moreno
Keyword(s):  
Singular Value Decomposition ◽  
Concept Lattice ◽  
Singular Value ◽  
Morphological Processing ◽  
Formal Concept ◽  
Complete Lattices ◽  
The Matrix ◽  
Dense Sets ◽  
Left And Right ◽  
Value Decomposition

In this paper, we provide a basic technique for Lattice Computing: an analogue of the Singular Value Decomposition for rectangular matrices over complete idempotent semifields (i-SVD). These algebras are already complete lattices and many of their instances—the complete schedule algebra or completed max-plus semifield, the tropical algebra, and the max-times algebra—are useful in a range of applications, e.g., morphological processing. We further the task of eliciting the relation between i-SVD and the extension of Formal Concept Analysis to complete idempotent semifields (K-FCA) started in a prior work. We find out that for a matrix with entries considered in a complete idempotent semifield, the Galois connection at the heart of K-FCA provides two basis of left- and right-singular vectors to choose from, for reconstructing the matrix. These are join-dense or meet-dense sets of object or attribute concepts of the concept lattice created by the connection, and they are almost surely not pairwise orthogonal. We conclude with an attempt analogue of the fundamental theorem of linear algebra that gathers all results and discuss it in the wider setting of matrix factorization.

Improving TF-IDF with Singular Value Decomposition (SVD) for Feature Extraction on Twitter

2017 ◽  
Author(s):  
Ammar Ismael Kadhim ◽  
Yu-N Cheah ◽  
Inaam Abbas Hieder ◽  
Rawaa Ahmed Ali
Keyword(s):  
Feature Extraction ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Value Decomposition

Multi-scale singular value decomposition polarization image fusion defogging algorithm and experiment

2020 ◽  
Vol 13 (6) ◽  
pp. 1-10
Author(s):  
ZHOU Wen-zhou ◽  
◽  
FAN Chen ◽  
HU Xiao-ping ◽  
HE Xiao-feng ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Image Fusion ◽  
Singular Value ◽  
Multi Scale ◽  
Polarization Image ◽  
Value Decomposition
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