Variable dimension data? • /r/MachineLearning

You could do K-nearest neighbor's interpolation to give the empty 0 values a "guess" to how they would look like to the nearest neighbors. How well this would work is really just based on the properties of the data. If dimension k can be predicted by some association with a dimension j, and this relationship with k and j is fairly strong throughout the data, then it's worth trying. If it's all over the place, this hack won't help at all, perhaps it would even make very unreliable predictions.