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 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
Download PDF |
EA2019-104 SIP2019-106 SP2019-53 |
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 |
Keyword(6) |
|
Keyword(7) |
|
Keyword(8) |
|
1st Author's Name |
Nobutaka Ono |
1st Author's Affiliation |
Tokyo Metropolitan University (TMU) |
2nd Author's Name |
|
2nd Author's Affiliation |
() |
3rd Author's Name |
|
3rd Author's Affiliation |
() |
4th Author's Name |
|
4th Author's Affiliation |
() |
5th Author's Name |
|
5th Author's Affiliation |
() |
6th Author's Name |
|
6th Author's Affiliation |
() |
7th Author's Name |
|
7th Author's Affiliation |
() |
8th Author's Name |
|
8th Author's Affiliation |
() |
9th Author's Name |
|
9th Author's Affiliation |
() |
10th Author's Name |
|
10th Author's Affiliation |
() |
11th Author's Name |
|
11th Author's Affiliation |
() |
12th Author's Name |
|
12th Author's Affiliation |
() |
13th Author's Name |
|
13th Author's Affiliation |
() |
14th Author's Name |
|
14th Author's Affiliation |
() |
15th Author's Name |
|
15th Author's Affiliation |
() |
16th Author's Name |
|
16th Author's Affiliation |
() |
17th Author's Name |
|
17th Author's Affiliation |
() |
18th Author's Name |
|
18th Author's Affiliation |
() |
19th Author's Name |
|
19th Author's Affiliation |
() |
20th Author's Name |
|
20th Author's Affiliation |
() |
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 |
6 |
Date of Issue |
2020-02-24 (EA, SIP, SP) |
|