Suppose I want to train a neural network on some multivariate data. This data is not linearly separable, and will require a non-linear, multi-layer neural network for effective classification.
Each data vector consists of two parts. The first part represents an observation, and the second part represents the day on which that observation was made. Now, if observations of one class are made on the same day, then I know that there is some correlation between the observation data. However, if observations of the same class are made on different days, I know that there is no correlation between the observation data. Effectively, every class-day pair could be treated as a separate class, and a new classifier could be trained on these classes.
So there are two ways of training this neural network. The first method is to train a single neural network, taking in both parts of the data vector. The second method is to train multiple neural networks, one for each day, taking in only the observation part of the data vector.
My question is: Would the second method necessarily perform better than the first?