Support Vector Machines

SVMs are cousins of neural networks, and in fact a certain kind of SVM is exactly the same as a multi layer perceptron. But SVMs originate from the world of mathematics rather than biology. They work by automatically dividing a set of values (vectors) into two classes – effectively figuring out the best straight line that can separate the values from each other. When several values are used in each vector, this line becomes a plane, or more commonly, a hyperplane (a plane in more than 3 dimensions). But this only provides a linear (straight-line) separation between the vectors, so the trick used by SVMs is to use a kernel function, which maps vectors onto a new twisted space where they can then be separated by a hyperplane. (Mapping the flat plane backwards to the old space would twist the plane until it was a lumpy and convoluted surface, able to separate the overlapping data points.) An SVM with a sigmoid kernel function is equivalent to a two layer perceptron neural network.

So SVMs work because they are able to use a simple hyperplane in combination with kernel functions to separate data. And if you can separate data into two classes then you can use the computer to learn. For example, one set of eye movements corresponds to you finding a passage of text relevant; another set of eye movements corresponds to you finding the text irrelevant. SVMs can distinguish between the two sets, and once it has learnt how, it can predict if future eye movements will correspond to you finding text relevant or not.

If these scientists are successful, future Internet search engines may involve just speaking a word and then glancing at the results, with every movement of your eyes fine- tuning the search until you find exactly what you want. For some, the mind-reading technology of eye tracking and machine learning may seem alarming. Mental images of the scene from the movie Minority Report may spring to mind, with electronic posters automatically detecting who we are and force-feeding us irresistible advertisements tailored to our every glance and mood. This may be highly desirable for companies wishing to sell their products to us, but you would always be able to escape. If eye tracking ever did become as ubiquitous and intrusive as television or Internet advertisements, you could always block the systems with a pair of dark glasses.

The scientists involved with this work are aware of these issues. Kai Puolamaki, another of the organisers of the original competition acknowledges, “The privacy issues have to of course be taken seriously. In this sense the eye movements are no different than other personal data stored in hard drives and sent through the net.” Luckily, your eye movement data without the machine learning software that interprets them are unlikely to be as easily exploited as information such as your emails or typing on a keyboard, so the movement of your eyes will always be more secure than your fingers.

One method they used was support vector machines – a method of statisticalmachine learning that happens to be a cousin of neural networks. It is a powerful technique, but even so, this was a tremendously challenging task.

“When we first started we thought there was no way it could work… but actually for some topics it performed amazingly well,” says Hardoon. The main problems with accuracy were actually caused by the test subjects themselves. The system was learning to understand how we skim-read text in order to pick out just a few key words – this is how we determine if something is useful to us. But this meant that if any test subject decided to read the whole passage of text, the computer couldn’t tell if the person found specific words in the text relevant or not. According to Hardoon, because computer scientist “geeks” were used in the tests, “they were too interested in subjects such as astronomy and so read the whole text, spoiling the experiment.” For subjects on sport, the tests were much more successful.

The feasibility study has now been expanded into a full-scale project (PINView – Personal Information Navigator adapting through Viewing), under negotiation for funding by the European Union. The ambitious study aims to link several novel forms of input, including speech recognition and the analysis of eye movements, to a search engine.

Consequently, the researchers prefer to take an optimistic view. In the words of Samuel Kaski, “I think it would make a lot of sense to integrate eye tracking technology to computer systems in the future… gaze direction is special because it is tied very closely to our attention and intentions.”

The goal of researchers like Kaski, Puolamaki, Hardoon and Shawe-Taylor is to help the public find what they want with the minimum of difficulty. Ideally this technology will be the perfect way to enable us to navigate through the vast and ever-growing information that surrounds us today. Before long, the right information for you may be just a glance away.

Resources:
PASCAL: http://www.pascal-network.org/
Eve Movements Challenge: http://www.pascal-network.org/Challenges/IREM/
Workshop: http://www.cis.hut.fi/inips2005/
Pump-priming project: http://www.cis.hut.fi/projects/mi/pump06
Eyetools: http://www.eyetools.com/