Non-linear Techniques for Dimension Reduction

2009 ◽  
pp. 1003-1007 ◽  
Author(s):  
Jian Yang ◽  
Zhong Jin ◽  
Jingyu Yang
Keyword(s):  
Dimension Reduction ◽  
Non Linear

[P1-261]: TRACKING ALZHEIMER's DISEASE PROGRESSION BY NON-LINEAR DIMENSION REDUCTION OF BRAIN MRI FEATURES

2017 ◽  
Vol 13 (7S_Part_7) ◽  
pp. P349-P350
Author(s):  
Kangwon Seo ◽  
Rong Pan ◽  
Kewei Chen ◽  
Pradeep Thiyyagura
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dimension Reduction ◽  
Disease Progression ◽  
Linear Dimension ◽  
Brain Mri ◽  
Mri Features ◽  
Non Linear

scholarly journals Learning Geographical Manifolds: A Kernel Trick for Geographical Machine Learning

2019 ◽  
Author(s):  
Levi John Wolf ◽  
Elijah Knaap
Keyword(s):  
Machine Learning ◽  
Dimension Reduction ◽  
Manifold Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Dimensional Structure ◽  
Geographic Scale ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Non Linear

Dimension reduction is one of the oldest concerns in geographical analysis. Despite significant, longstanding attention in geographical problems, recent advances in non-linear techniques for dimension reduction, called manifold learning, have not been adopted in classic data-intensive geographical problems. More generally, machine learning methods for geographical problems often focus more on applying standard machine learning algorithms to geographic data, rather than applying true "spatially-correlated learning," in the words of Kohonen. As such, we suggest a general way to incentivize geographical learning in machine learning algorithms, and link it to many past methods that introduced geography into statistical techniques. We develop a specific instance of this by specifying two geographical variants of Isomap, a non-linear dimension reduction, or "manifold learning," technique. We also provide a method for assessing what is added by incorporating geography and estimate the manifold's intrinsic geographic scale. To illustrate the concepts and provide interpretable results, we conducting a dimension reduction on geographical and high-dimensional structure of social and economic data on Brooklyn, New York. Overall, this paper's main endeavor--defining and explaining a way to "geographize" many machine learning methods--yields interesting and novel results for manifold learning the estimation of intrinsic geographical scale in unsupervised learning.

The effect of non-linear dimension reduction on Gabor filter bank feature space

2013 ◽  
Vol 134 (5) ◽  
pp. 4069-4069 ◽  
Author(s):  
Hitesh A. Gupta ◽  
Anirudh Raju ◽  
Abeer Alwan
Keyword(s):  
Dimension Reduction ◽  
Filter Bank ◽  
Linear Dimension ◽  
Gabor Filter ◽  
Feature Space ◽  
Non Linear ◽  
Gabor Filter Bank
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