Body: <p>I'm working on a problem that given a dataset; where each train example is a binary matrix <span class="math-container">$X_i$</span> with dimension <span class="math-container">$(N_i,D_i)$</span> (think a training example is a feature matrix) each entry is either 1 or 0.</p>
  
  <p>Also, each training example <span class="math-container">$X_i$</span> has a corresponding label <span class="math-container">$Y_i$</span> is a correlation matrix dimension <span class="math-container">$(D_i,D_i)$</span></p>
  
  <p>My goal is to construct a model that take the input <span class="math-container">$X_i$</span> and coutput <span class="math-container">$\hat{Y}_i$</span> that matches with <span class="math-container">$Y_i$</span>.</p>
  
  <p>The major challenges here is that; each training example <span class="math-container">$X_i$</span> can have different dimensionality <span class="math-container">$(N_i,D_i)$</span> different data points and different number of variables.</p>
  
  <p>I'm wondering if there is any neural network architecture can handle case like this ?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-02-21T01:30:20.630Z
Comments.Text: Hi and welcome to AI SE! The question in the title is not exactly the same as the question in the body. So, what's your actual question?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-02-21T01:55:00.407Z
Comments.Text: @nbro thanks for the reply; I've re-edited the question. So the question is mainly about what kind of neural network to use to solve the problem I described
Comments.UserDisplayName: ElleryL
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-02-21T02:16:49.040Z
Comments.Text: If the difference in dimensional is only in 2 dimensions, you can just resize your input to one your model accepts. Some information might be lost, but if your input is like an image for example, the important features will remain. After that, you can use a CNN.
Comments.UserDisplayName: Recessive
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-02-21T03:05:20.257Z
Comments.Text: @Recessive; hi thanks for the reply; but how do resize the input ? let's say that one data example is (1000,20); and another one is (681,74). There couldn't be any way to resize them into the same shape.
Comments.UserDisplayName: ElleryL
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-02-21T03:22:20.747Z
Comments.Text: @ElleryL Sure there is, it just wont be perfect. Here's https://imgur.com/a/GaY3j5N my beautiful art in (1000,20) pixels, and here it is again resized to (681, 74) https://imgur.com/a/aSECwmR. You can see that even though it's resized, most of the original features are kept. If you have more even dimensions (like a box), resizing has even less of an effect.
Comments.UserDisplayName: Recessive
Tags: <neural-networks><deep-learning>
Title: Can neural network be trained to solve this problem?
UserDisplayName: ElleryL
ThreadText: # Can neural network be trained to solve this problem?
  
  (Asked by: username_0 on 2020-02-21)
  
  I'm working on a problem that given a dataset; where each train example is a binary matrix $X\_i$ with dimension $(N\_i,D\_i)$ (think a training example is a feature matrix) each entry is either 1 or 0.
  
  Also, each training example $X\_i$ has a corresponding label $Y\_i$ is a correlation matrix dimension $(D\_i,D\_i)$
  
  My goal is to construct a model that take the input $X\_i$ and coutput $\hat{Y}\_i$ that matches with $Y\_i$.
  
  The major challenges here is that; each training example $X\_i$ can have different dimensionality $(N\_i,D\_i)$ different data points and different number of variables.
  
  I'm wondering if there is any neural network architecture can handle case like this ?
  
  **Tags:** neural-networks, deep-learning
  > **Comments:**
  > **username_1**: Hi and welcome to AI SE! The question in the title is not exactly the same as the question in the body. So, what's your actual question?  
  > **username_0**: @nbro thanks for the reply; I've re-edited the question. So the question is mainly about what kind of neural network to use to solve the problem I described  
  > **username_2**: If the difference in dimensional is only in 2 dimensions, you can just resize your input to one your model accepts. Some information might be lost, but if your input is like an image for example, the important features will remain. After that, you can use a CNN.  
  > **username_0**: @Recessive; hi thanks for the reply; but how do resize the input ? let's say that one data example is (1000,20); and another one is (681,74). There couldn't be any way to resize them into the same shape.  
  > **username_2**: @ElleryL Sure there is, it just wont be perfect. Here's https://imgur.com/a/GaY3j5N my beautiful art in (1000,20) pixels, and here it is again resized to (681, 74) https://imgur.com/a/aSECwmR. You can see that even though it's resized, most of the original features are kept. If you have more even dimensions (like a box), resizing has even less of an effect.  
  
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  ## 0 answers
  
  