What are main differences between sparse data and missing data? And how does it influences machine learning? More specifically, what effect sparse data and missing data have on classification algorithms and regression (predicting numbers) type of algorithms. I'm talking about a situation, where percentage of missing data is significant and we can't drop the rows containing missing data.
For the ease of understanding, I'll describe this using an example. Let's say that you are collecting data from a device which has 12 sensors. And you have collected data for 10 days.
This is called sparse data because most of the sensor outputs are zero. Which means those sensors are functioning properly but the actual reading is zero. Although this matrix has high dimensional data (12 axises) it can be said that it contains less information.
In this case, you can see that you cannot use data from Sensor1 and Sensor6. Either you have to fill data manually without affecting the results or you have to redo the experiment.