Can neural networks handle high-dimensional data?

Can neural networks handle high-dimensional data?

Abstract. Training deep neural networks (DNNs) on high-dimensional data with no spatial structure poses a major computational problem. Our results demonstrate that DNNs with RP layer achieve competitive performance on high-dimensional real-world datasets.

What is the problem with high-dimensional data?

(iii)For high-dimensional data, datasets tend to be unstructured, which may pose extra challenges to use. In addition, noise and uncertainties often exist in big datasets. Such noisy data can become more challenging to process and to apply any proper data mining techniques.

Why is my neural network not accurate?

Your Network contains Bad Gradients. You Initialized your Network Weights Incorrectly. You Used a Network that was too Deep. You Used the Wrong Number of Hidden Units.

Why CNN is better than neural network?

CNN is considered to be more powerful than ANN, RNN. RNN includes less feature compatibility when compared to CNN. Facial recognition and Computer vision. Facial recognition, text digitization and Natural language processing.

What is a high dimensional data set?

High Dimensional means that the number of dimensions are staggeringly high — so high that calculations become extremely difficult. With high dimensional data, the number of features can exceed the number of observations. One person (i.e. one observation) has millions of possible gene combinations.

What is highly dimensional data?

Which is the best technique of data has many dimensions?

The best way to go higher than three dimensions is to use plot facets, color, shapes, sizes, depth and so on. You can also use time as a dimension by making an animated plot for other attributes over time (considering time is a dimension in the data).

Why is CNN over RNN?

CNN is considered to be more powerful than RNN. RNN includes less feature compatibility when compared to CNN. This network takes fixed size inputs and generates fixed size outputs. RNN unlike feed forward neural networks – can use their internal memory to process arbitrary sequences of inputs.

Why do we flatten the data in neural networks?

You don’t have to flatten if you’re using a Convolutional Neural Network (CNN). But if you’re using regular “Dense” or “Linear” layers of weights, the answer is: Because you want to couple information that exists vertically as well as horizontally.

How much data do you need to train a neural network?

If you are training a net from scratch (i.e. not finetuning), you probably need lots of data. For image classification, people say you need a 1000 images per class or more. 10. Make sure your batches don’t contain a single label This can happen in a sorted dataset (i.e. the first 10k samples contain the same class).

What to do if your neural network is not working?

Try passing random numbers instead of actual data and see if the error behaves the same way. If it does, it’s a sure sign that your net is turning data into garbage at some point. Try debugging layer by layer /op by op/ and see where things go wrong.

What causes a neural network to underfit?

Augmentation has a regularizing effect. Too much of this combined with other forms of regularization (weight L2, dropout, etc.) can cause the net to underfit. 14. Check the preprocessing of your pretrained model If you are using a pretrained model, make sure you are using the same normalization and preprocessing as the model was when training.