Advantages of Unsupervised Learning

In the post on supervised and unsupervised learning it was mentioned that unsupervised learning doesn’t need labelled data and results in features being detected. Features are patterns in the data.

The main advantage of unsupervised learning is labelled data isn’t required. As labelling usually has to be performed manually this saves a significant amount of time. In some situations, the quantity of the data means it’s not physically possible to manually classify the data.

As the input data isn’t labelled there’s no extra human influence on the input and hence no human error or human bias.

The model detects features in the data that can be sub-features, features of interest or a mix of features. For example human gesture recognition produces features of interest (e.g. walking, running, jumping) sub-features (movement upwards, downwards) and combinations of features (jumping while running).

As sub-features are being detected, the same model can sometimes be used to detect features of interest for which it wasn’t trained on. For example, a human gesture model trained on running, sitting down and walking might detect enough parts of the movement to also allow a combination of features to signify lying down.

As the model isn’t directed (supervised) to find specific things, it can also find hidden features in the data. For example, a model trained to find features of interest in vehicle driving (turning left, turning right, stopped, accelerating, slowing) might inadvertently also detect potholes in the road.

More usefully, unsupervised models can be purposely used to find hidden features in the data that a human can’t correlate and hence detect. For example, it might be used to find:

  • a pattern in a vulnerable person’s movement that indicates they are about to fall,
  • a pattern in complex sensing of an industrial motor that indicates it is about to fail,
  • a hidden pattern in share price data that indicates you should sell.

The above presuppose there’s enough information in the data to detect such things. The latter share selling detection is a case in point as there’s often insufficient detail in financial data for such determination.

This is where domain experts are helpful as they can help direct what might be possible and advise on extra data that might be required. For example, the share selling detection is more likely to work if you added in weather in Columbia to a coffee company share price buy/sell model.

As unsupervised learning looks for features rather than, for supervised learning, specific patterns in the data, it’s more likely an existing pre-learnt model can be re-used in a new domain. For example, a model taught with lots of human gestures (running, walking, jumping) might become expert in movements of an accelerometer and be used for detecting movements of a car (left, right, slowing, speeding up). Re-use of a model can save the considerable time required for the learning part of machine learning.