6
votes
What are the purposes of autoencoders?
It is important to think about what sort of patterns in the data are being represented.
Suppose that you have a dataset of greyscale images, such that every image is a uniform intensity. As a human ...
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5
votes
How do AI researchers imagine higher dimensions?
The most I can visualize or perceive are 4 dimensions. Yes, 4, because I can also watch videos (which have 3 spatial dimensions and 1 temporal one). Remember Einstein's spacetime?
When dealing with $n$...
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4
votes
Accepted
What are examples of approaches to dimensionality reduction of feature vectors?
Dimensionality reduction could be achieved by using an Autoencoder Network, which learns a representation (or Encoding) for the input data. While training, the reduction side (Encoder) reduces the ...
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3
votes
What are the purposes of autoencoders?
A use case of autoencoders (in particular, of the decoder or generative model of the autoencoder) is to denoise the input. This type of autoencoders, called denoising autoencoders, take a partially ...
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3
votes
Accepted
Why does PCA of the vertices of a hexagon result in principal components of equal length?
Assuming that the $6$ vertices of the hexagon are on the unit circle,
...
2
votes
What are the purposes of autoencoders?
PCA is a linear method that creates a transformation that is capable of changing the vectors projections (changing axis)
Since PCA looks for the direction of maximum variance it usually have high ...
2
votes
How classification neural nets are different from simple dimension reduction + clustering?
PCA is linear, NN are nonlinear, more generally it's a universal function approximator.
That said basic NN are not terribly usefull, the real value of NN is for structured data which is obvious for us ...
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2
votes
When using PCA for dimensionality reduction of the feature vectors to speed up learning, how do I know that I'm not letting the model overfit?
I'm not sure if I understood your question correctly, but here's my take anyway.
So, PCA is a technique that you can apply to data to reduce the number of features.
In return, (i) this can speed-up ...
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2
votes
What are examples of approaches to dimensionality reduction of feature vectors?
Some examples of dimensionality reduction techniques:
Linear methods
Non-linear methods
Graph-based methods("Network embedding")
PCA CCA ICA SVD LDA NMF
Kernel PCA GDA Autoencoders t-SNE ...
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1
vote
Accepted
Clustering by using Locality sensitive hashing *after* Random projection
I think the following is the way to look at your question.
RP reduces dimensionality based on distance.
LSH clusters data based on a similar distance method used in RP.
The primary function of any ...
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1
vote
Accepted
Is it generally advisable to have a low dimensional action space in Reinforcement Learning?
Since the question may not be answered unambiguously in general, I will use the given example as a guide. As you correctly write, a large dimensionality leads to a very large solution space because of ...
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1
vote
Accepted
Is it theoretically possible (or impossible) that principal component analysis worsens the performance of the model?
PCA works well where data sample space is linear. If data sample space is not linear or it is manifold data then model without PCA may perform better than model using PCA.
In the given image you can ...
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1
vote
Is it theoretically possible (or impossible) that principal component analysis worsens the performance of the model?
PCA can make models worse, imagine data points scattered along two elongated parallel rectangles. The axis with the greatest variation will be parallel to the rectangles but doesn't provide any ...
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1
vote
What are the purposes of autoencoders?
The decoder half is necessary in order to compute the loss function for training the network. Similar to how the 'adversary' is still necessary in a GAN even if you are only interested in the ...
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