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I worked with Chernoff faces long time back and love how this is an interesting way to visualize how discriminative your features are. The idea is that you tak
by aecs99 9y ago
I worked with Chernoff faces long time back and love how this is an interesting way to visualize how discriminative your features are.
The idea is that you take features of your dataset, and use those to represent a face. Say for example, you want to classify 100 people based on different features. And let's say you've collected 15 features for each person (e.g., height, weight, shoulder width, length of first name, length of last name, type of car driven, etc.). Now try mapping each of these features to Chernoff faces. You'd map it in the following manner: height->area of face, weight->shape of face, shoulder width->length of nose, length of first name->location of mouth, length of last name->curve of smile, type of car driven->width of mouth, etc.
Once you've mapped in that fashion and visualize the faces, you can observe how discriminative your features are. How do you interpret this? If your Chernoff faces tend to show a lot of variation in expression (e.g., smiling vs. sad), you say the length of last name is more discriminative. On the other hand, if the faces all appear to have same area, your first feature (i.e., height) is not very discriminative.
Other features used for Chernoff faces could be:
location, separation, angle, shape, and width of eyes;
location, and width of pupil; location,
angle, and width of eyebrow, etc.
One drawback (as listed in the Wikipedia page) is that we humans perceive the importance of these faces by the way in which variables are mapped to the Chernoff facial features. If the feature mapping is not carefully chosen, your largest varying feature may be ignored because we appreciated the change in expression more than the change in eyebrow length.
- amelius 9y ago> this is an interesting way to visualize how discriminative your features are. I don't get why that is easier or more revealing than doing a principal component analysis (?)
- aecs99 9y agoI agree. I don't think this isn't any more revealing. Just a different way of visualizing. And there is also the drawback of a whole new interpretation when you re-map your input features to Chernoff facial features.
- classichasclass 9y agoThat's what I'm struggling with. Take the example on the Wikipedia page: I don't know what the faces mean for each judge. Is the judge a jerk? Does the judge take too long with their arraignments? Is this a value, er, judgment on the legal sufficiency of their determinations? If the primary utility is to be able to quickly visually discriminate values once you know how they're encoded into facial features, then I can see the value, but again you'd have to know the encoding. Or have I missed the point completely?
- aecs99 9y agoYes, the primary utility is to understand how discriminative your features are. There is no meaning of what each face represents. Checkout the Chernoff Fish demo posted below by the user meagher here: https://news.ycombinator.com/item?id=16664051 https://news.ycombinator.com/item?id=16664051. Play with different features, say for example, 'performance'. When you change the value of 'performance', the eye size changes. However, the eye size doesn't mean anything except for you to visually understand variations in data. If 'performance' was mapped to, say, fin size, it doesn't change its meaning.
- classichasclass 9y agoThanks, I think I understand its utility a bit better now.
- davidgu 9y agoI wonder how this affects pattern recognition. Would people more readily recognize that say, "Large Spiky Orange Fish" strategies lead to greater returns, compared to if the strategies were presented as "Short | Value Investment | Large Market Capitalization"? This could also be an interesting way of eliminating inherent bias while leveraging human pattern recognition abilities. Represent values pictorially, and hide data labels.
- fjsolwmv 9y agoIt's not meant for professional data analysis use. It's a gimmick for kids or for printing in a magazine article.
- OscarCunningham 9y agoBecause humans are naturally good at recognizing faces and the differences between them.
- BjoernKW 9y agoBecause recognising faces is an innate human ability while calculating eigenvectors is not.
- IshKebab 9y agoThat's why we get computers to calculate eigenvectors and plot the result in a nice graph that humans are fine at interpreting.