Non-linear dimensionality reduction with input distances preservation

1999 ◽  
Author(s):  
L. Garrido
Keyword(s):  
Dimensionality Reduction ◽  
Non Linear ◽  
Linear Dimensionality Reduction

scholarly journals Training a neural network to learn other dimensionality reduction removes data size restrictions in bioinformatics and provides a new route to exploring data representations

2020 ◽  
Author(s):  
Alex Dexter ◽  
Spencer A. Thomas ◽  
Rory T. Steven ◽  
Kenneth N. Robinson ◽  
Adam J. Taylor ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Dimensionality Reduction ◽  
Computational Analysis ◽  
New Technologies ◽  
State Of The Art ◽  
Current State ◽  
Data Representations ◽  
Non Linear ◽  
Linear Dimensionality Reduction

AbstractHigh dimensionality omics and hyperspectral imaging datasets present difficult challenges for feature extraction and data mining due to huge numbers of features that cannot be simultaneously examined. The sample numbers and variables of these methods are constantly growing as new technologies are developed, and computational analysis needs to evolve to keep up with growing demand. Current state of the art algorithms can handle some routine datasets but struggle when datasets grow above a certain size. We present a training deep learning via neural networks on non-linear dimensionality reduction, in particular t-distributed stochastic neighbour embedding (t-SNE), to overcome prior limitations of these methods.One Sentence SummaryAnalysis of prohibitively large datasets by combining deep learning via neural networks with non-linear dimensionality reduction.

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