Hi I'm using neural network to solve a multi regression problem. I'm trying to predict continuous values, to be more specific I'm making a tracking algorithm to track the position of an Object, I'm trying to predict two values, the latitude and longitude of an Object. Now to calculate the loss of the Model there is some common functions like mean squared Error or mean absolute error etc.. but I'm wondering if I can use some custom function like this to calculate the distance between the two longitude and latitude values and then the loss would be the difference between the real distance (calculated from the real longitude and latitude) and the predicted distance (calculatd from the predicted longitude and latitude). this was some thoughts from me so I'm wondering if such an Idea would make sense?
anyone have an Idea whether this would work in my case better than using the mean squared error as a loss function?
I had another question in Mind. in my case I'm predicting two values (longitude and latitude) but is there a way to transform these two target values to only one value so that my neural network can learn better and faster? if yes which method should I use? should I calculate the summation of the two and make that as a new Target? does this make sense?