Combine two feature vectors for a correct input of a neural network Let's consider this scenario. I have two conceptually different video datasets, for example a dataset A composed of videos about cats and a dataset B composed of videos about houses. Now, I'm able to extract a feature vectors from both the samples of the datasets A and B, and I know that, each sample in the dataset A is related to one and only one sample in the dataset B and they belong to a specific class (there are only 2 classes).

For example:

Sample x1 AND sample y1 ---&gt; Class 1
Sample x2 AND sample y2 ---&gt; Class 2
Sample x3 AND sample y3 ---&gt; Class 1
and so on...


If I extract the feature vectors from samples in both datasets , which is the best way to combine them in order to give a correct input to the classifier (for example a neural network) ?

feature vector v1 extracted from x1 + feature vector v1' extracted from y1 ---> input for classifier

I ask this because I suspect that neural networks only take one vector as input, while I have to combine two vectors
The easiest way can be the concatenation of the feature vectors to create a single feature vector for each sample.

Assume the first sample is made of the pair $X_1$ and $Y_1$. Let the corresponding feature vectors for $X_1$ and $Y_1$ be $\textbf{v}_1$ and $\textbf{v}_2$, respectively.

$$
\textbf{v}_1 = [f_1, f_2, \ldots , f_n],\\
\textbf{v}_2 = [g_1, g_2, \ldots , g_m].
$$
Then, the first sample's feature can be defined as
$$
\textbf{v} = [f_1, f_2, \ldots , f_n, g_1, g_2, \ldots , g_m].
$$
Eventually, when you pass the latter feature vector to a machine learning model, it will try to capture the dependencies among all of these features, to learn a solution for your task of interest (i.e. classification).