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Paper Abstract and Keywords
Presentation 2020-03-02 10:35
Dimension reduction without multiplication in machine learning
Nobutaka Ono (TMU) EA2019-104 SIP2019-106 SP2019-53
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, we propose a dimension reduction method for machine learning by only selecting elements without multiplication. In machine learning, the dimension reduction is an important preprocessing that reduces unnecessary model parameters, and it alleviates over-fitting and improves learning speed. One of the basic and frequently used methods is principal component analysis (PCA). However, since PCA requires matrix multiplication, it is not suitable for a system with limited computational power such as hearing aids and embedded devices. The number of times of multiplication in PCA itself may cause a large calculation load. In this study, to reduce the amount of calculation in dimension reduction, we consider dimension reduction only by element selection. What is essential at this time is which elements of the input vector are selected. In this study, we consider an objective function defined as the reconstruction loss of a linear autoencoder, and this is formulated as a discrete optimization problem that selects the element that minimizes it. Also, we propose a method to solve this problem by sequentially replacing elements chosen so that the objective function becomes smaller. The calculation of the objective function includes the inverse matrix operation. An algorithm that significantly reduces the amount of computation using the inverse matrix lemma is described. Preliminary experimental results for video show the effectiveness of this method.
Keyword (in Japanese) (See Japanese page) 
(in English) dimension reduction / machine learning / element seelction / multiplication / computational cost / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 440, SIP2019-106, pp. 21-26, March 2020.
Paper # SIP2019-106 
Date of Issue 2020-02-24 (EA, SIP, SP) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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Conference Information
Committee SP EA SIP  
Conference Date 2020-03-02 - 2020-03-03 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Industry Support Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SIP 
Conference Code 2020-03-SP-EA-SIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Dimension reduction without multiplication in machine learning 
Sub Title (in English)  
Keyword(1) dimension reduction  
Keyword(2) machine learning  
Keyword(3) element seelction  
Keyword(4) multiplication  
Keyword(5) computational cost  
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1st Author's Name Nobutaka Ono  
1st Author's Affiliation Tokyo Metropolitan University (TMU)
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Speaker Author-1 
Date Time 2020-03-02 10:35:00 
Presentation Time 25 minutes 
Registration for SIP 
Paper # EA2019-104, SIP2019-106, SP2019-53 
Volume (vol) vol.119 
Number (no) no.439(EA), no.440(SIP), no.441(SP) 
Page pp.21-26 
#Pages
Date of Issue 2020-02-24 (EA, SIP, SP) 


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