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Title:Application of CNN auto-encoder in Spatial Dimensionality Reduction of Mass Spectrometry Imaging Data
Author(s):Liu, Liyu
Contributor(s):Ochoa, Idoia
Degree:B.S. (bachelor's)
Subject(s):CNN Auto-Encoder
Spatial Dimensionality Reduction
Mass Spectrometry Imaging Data
Abstract:Methods to reduce data spatial dimension so as to reduce the computational cost while preserving the original spatial information are an important research field in machine learning. Methods such as PCA (Principal Component Analysis) and NMF (non-negative matrix factorization) are widely used for dimension reduction. There are also works on using auto-encoder based on fully connected layers for dimension reduction. However, fully connected layers have extremely high computational cost and are therefore time consuming. In this work, we propose a CNN layers based auto-encoder to reduce the dimension of data input. We applied our network on Mouse Urinary Bladder Data-set with fixed dimensions of 5180* 38440. We compared the performance regarding execution time, MSE and spatial variance with those of PCA, NMF and FC auto-encoder. Our result shows that although we sacrificed MSE accuracy compared with PCA, NMF and FC auto-encoder, we preserved the most spatial information compared with raw input. Also, our CNN layers based auto-encoder increased the computational speed by almost 4x compared to that of FC auto-encoder. Our result promotes the potential of using the reduced feature for further calculation.
Issue Date:2020-12
Genre:Dissertation / Thesis
Date Available in IDEALS:2021-01-04

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