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 ...
Josiah's user avatar
  • 169
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 $n$...
nbro's user avatar
  • 40.2k
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 ...
s_bh's user avatar
  • 360
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 ...
nbro's user avatar
  • 40.2k
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 ...
Pedro Henrique Monforte's user avatar
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 ...
FourierFlux's user avatar
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 ...
SpiderRico's user avatar
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 ...
brazofuerte's user avatar
  • 1,031
2 votes

the best choice to reduce a features vector

Feature selection -- the case in which the features are highly correlated is the prototypical case in which you want to select a subset of independent features that allows for an equal performance. ...
Peblo's user avatar
  • 31
2 votes
Accepted

What is $\mathbf{S}$ (sample covariance matrix) in image compression based on PCA?

Good question! There's actually some ambiguity here: it's possible to consider the lower-dimensional projection with respect to the pixels within a single image or across a dataset of images. A ...
Alexander Wan's user avatar
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 ...
Arun Aniyan's user avatar
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 ...
dexteritas's user avatar
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 ...
VIJAY's user avatar
  • 36
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 ...
FourierFlux's user avatar
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 ...
brazofuerte's user avatar
  • 1,031

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