153

Gifu University

Motoki Shiga

Recently developed technologies on measurement engineering, such as scanning transmission electron

microscopes, have enabled us to monitor characterizations simultaneously. Thus analyzing such large datasets

recoded in tensors (multi-dimensional data arrays) is becoming more and more important. This research

project has developed nonnegative tensor (or matrix) factorization methods to combine auxiliary information

sources and physical conditions and to extend noise models from an additive Gaussian model to more general

models including a Poisson noise model. Numerical experimental results using a STEM-EELS microscopy

dataset demonstrated effectiveness of the developed method.

2016年3月9日 受理* 豊田理研スカラー 岐阜大学工学部電気電子・情報工学科情報コース

補助情報を用いるテンソル因子化法における雑音モデルの一般化

補助情報を用いるテンソル因子化法に おける雑音モデルの一般化

＊志　賀　元　紀＊

Gifu University

*Motoki Shiga*

図1：非負値行列分解の概念図

154

(STEM-EELS)

REFERENCES

(a-1) (a-2) (b-1)

(b-2) (b-3)

補助情報を用いるテンソル因子化法における雑音モデルの一般化