<p>You can try Dimensionality Reduction Techniques like <a href="http://en.wikipedia.org/wiki/Principal_component_analysis" rel="nofollow">PCA</a> or <a href="http://en.wikipedia.org/wiki/Linear_discriminant_analysis" rel="nofollow">LDA</a>.</p>

<p>There's <a href="http://www.cc.gatech.edu/~isbell/tutorials/dimred-survey.pdf" rel="nofollow">this</a> survey by Fodor, but it involves some math, so you can look into this <a href="http://www.math.uwaterloo.ca/~aghodsib/courses/f06stat890/readings/tutorial_stat890.pdf" rel="nofollow">tutorial</a> as well.</p>

<p>Another thing you can do is Feature Selection in the preprocessing step. You can look into <a href="http://www.researchgate.net/publication/231175355_On_General_Definition_of_L1-norm_Support_Vector_Machines_for_Feature_Selection/file/79e415063b2049dc26.pdf" rel="nofollow">this paper</a> by Nguyen.</p>

<p>Also if you want an easy way out, you can use the <a href="http://featureselection.asu.edu/software.php" rel="nofollow">Software Package</a> from ASU. They have a fairly well documented <a href="http://featureselection.asu.edu/featureselection_techreport.pdf" rel="nofollow">report</a> on it as well.</p>

<p>Hope this somewhat helps.
Have fun Learning :)</p>

CC BY-SA 3.0
2013-08-23T14:48:18.640Z
2013-08-23T14:48:18.640Z
Subhayan
<p>I have this huge dimensional data with 31200 features and only 6000 examples. I want to learn a neural network  that can find the non linear relation between the input and outputs. However, I have huge number of features. So wouldn't it cause problems in neural network?</p>

<machine-learning><ai.artificial-intel>
Mapping input and output sequences using neural networks in high dimensional data
user34790
# Mapping input and output sequences using neural networks in high dimensional data

(Asked by: username_0 on 2013-08-23)

I have this huge dimensional data with 31200 features and only 6000 examples. I want to learn a neural network that can find the non linear relation between the input and outputs. However, I have huge number of features. So wouldn't it cause problems in neural network?

**Tags:** machine-learning, ai.artificial-intel


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## 1 answers

### Answer by username_1 on 2013-08-23. Score: 0

You can try Dimensionality Reduction Techniques like [PCA](http://en.wikipedia.org/wiki/Principal_component_analysis) or [LDA](http://en.wikipedia.org/wiki/Linear_discriminant_analysis).

There's [this](http://www.cc.gatech.edu/~isbell/tutorials/dimred-survey.pdf) survey by Fodor, but it involves some math, so you can look into this [tutorial](http://www.math.uwaterloo.ca/~aghodsib/courses/f06stat890/readings/tutorial_stat890.pdf) as well.

Another thing you can do is Feature Selection in the preprocessing step. You can look into [this paper](http://www.researchgate.net/publication/231175355_On_General_Definition_of_L1-norm_Support_Vector_Machines_for_Feature_Selection/file/79e415063b2049dc26.pdf) by Nguyen.

Also if you want an easy way out, you can use the [Software Package](http://featureselection.asu.edu/software.php) from ASU. They have a fairly well documented [report](http://featureselection.asu.edu/featureselection_techreport.pdf) on it as well.

Hope this somewhat helps.
Have fun Learning :)


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