scholarly journals Comparative Analysis on Dimension Reduction Algorithm of Principal Component Analysis and Singular Value Decomposition for Clustering

2020 ◽  
Vol 1641 ◽  
pp. 012101
Author(s):  
Elly Muningsih ◽  
Hidayat Muhammad Nur ◽  
Fabriyan Fandi Dwi Imaniawan ◽  
Saifudin ◽  
Vembria Rose Handayani ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Comparative Analysis ◽  
Singular Value Decomposition ◽  
Dimension Reduction ◽  
Principal Component ◽  
Component Analysis ◽  
Singular Value ◽  
Reduction Algorithm ◽  
Value Decomposition

scholarly journals WEB NEWS DOCUMENTS CLUSTERING IN INDONESIAN LANGUAGE USING SINGULAR VALUE DECOMPOSITION-PRINCIPAL COMPONENT ANALYSIS (SVDPCA) AND ANT ALGORITHMS

2016 ◽  
Vol 9 (1) ◽  
pp. 17
Author(s):  
Arif Fadllullah ◽  
Dasrit Debora Kamudi ◽  
Muhamad Nasir ◽  
Agus Zainal Arifin ◽  
Diana Purwitasari
Keyword(s):  
Principal Component Analysis ◽  
Singular Value Decomposition ◽  
Dimension Reduction ◽  
Document Clustering ◽  
Principal Component ◽  
Component Analysis ◽  
Singular Value ◽  
Ant Algorithms ◽  
Value Decomposition ◽  
Trial Phase

Ant-based document clustering is a cluster method of measuring text documents similarity based on the shortest path between nodes (trial phase) and determines the optimal clusters of sequence document similarity (dividing phase). The processing time of trial phase Ant algorithms to make document vectors is very long because of high dimensional Document-Term Matrix (DTM). In this paper, we proposed a document clustering method for optimizing dimension reduction using Singular Value Decomposition-Principal Component Analysis (SVDPCA) and Ant algorithms. SVDPCA reduces size of the DTM dimensions by converting freq-term of conventional DTM to score-pc of Document-PC Matrix (DPCM). Ant algorithms creates documents clustering using the vector space model based on the dimension reduction result of DPCM. The experimental results on 506 news documents in Indonesian language demonstrated that the proposed method worked well to optimize dimension reduction up to 99.7%. We could speed up execution time efficiently of the trial phase and maintain the best F-measure achieved from experiments was 0.88 (88%).

scholarly journals Dimensionality Reduction of Dynamic Mesh Animations Using HO-SVD

2013 ◽  
Vol 3 (4) ◽  
pp. 277-289 ◽  
Author(s):  
Michał Romaszewski ◽  
Piotr Gawron ◽  
Sebastian Opozda
Keyword(s):  
Principal Component Analysis ◽  
Singular Value Decomposition ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Component Analysis ◽  
Higher Order ◽  
Singular Value ◽  
3D Mesh ◽  
Reduction Of Dimensionality ◽  
Value Decomposition

Abstract This work presents an analysis of Higher Order Singular Value Decomposition (HOSVD) applied to reduction of dimensionality of 3D mesh animations. Compression error is measured using three metrics (MSE, Hausdorff, MSDM). Results are compared with a method based on Principal Component Analysis (PCA) and presented on a set of animations with typical mesh deformations.

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