Laplacian Eigenmaps for Differentiating Trace Elements in Normal and Malignant Breast Patients
碩士 === 國立中興大學 === 應用數學系所 === 105 === In this thesis, we mainly focus on a nonlinear dimension reduction technique of Laplacian Eigenmaps (LE), and also use two techniques for dimension reduction which are Principal Component Analysis (PCA) and Locally Linear Embedding(LLE) respectively. We analyze d...
Main Authors: | Ming-Kuei Chi, 紀銘貴 |
---|---|
Other Authors: | 施因澤 |
Format: | Others |
Language: | zh-TW |
Published: |
2017
|
Online Access: | http://ndltd.ncl.edu.tw/handle/87975663377157457629 |
Similar Items
-
PolSAR image classification based on Laplacian Eigenmaps and superpixels
by: Haijiang Wang, et al.
Published: (2017-11-01) -
Laplacian Eigenmaps Dimensionality Reduction Based on Clustering-Adjusted Similarity
by: Honghu Zhou, et al.
Published: (2019-10-01) -
The Chaotic Attractor Analysis of DJIA Based on Manifold Embedding and Laplacian Eigenmaps
by: Xiaohua Song, et al.
Published: (2016-01-01) -
Laplacian Eigenmaps Network-Based Nonlocal Means Method for MR Image Denoising
by: Houqiang Yu, et al.
Published: (2019-07-01) -
Locally Linear Embedding for DifferentiatingTrace Elements in Normal and Malignant Breast Patients
by: Hsu-Chen Wang, et al.
Published: (2017)