Revisiting Dimensionality Reduction (Conventional Methods with an Unconventional Approach)
Although I’ve talked about dimensionality reduction for data science in the corresponding video on Safari, covering various angles of the topic, I was never fully content with the methodologies out there. After all, all the good ones are fairly sophisticated, while all the easier ones are quite limited. Could there be a different (better) way of performing dimensionality reduction in a dataset? If so, what issue would such a method tackle?
First of all, conventional dimensionality reduction methods tend to come from Statistics. That’s great if the dataset is fairly simple, but methods like PCA focus on the linear relationships among the features, which although it’s a good place to start, it doesn’t cover all the bases. For example, what if features F1 and F2 have a non-linear relationship? Will PCA be able to spot that? Probably not, unless there is a strong linear component to it. Also, if F1 and F2 follow some strange distribution, the PCA method won’t work very well either.
What's more, what if you want to have meta-features that are independent to each other, yet still explain a lot of variance? Clearly PCA won’t always give you this sort of results, since for complex datasets the PCs will end up being tangled themselves. Also, ICA, a method designed for independent components, is not as easy to use since it’s hard to figure out exactly where to stop when it comes to selecting meta-features.
In addition, what’s the deal with outliers in the features? Surely they affect the end result, by changing the whole landscape of the features, breaking the whole scale equilibrium at times. Well, that’s one of the weak point of PCA and similar dimensionality reduction methods, since they require some data engineering before they can do their magic.
Finally, how much does each one of the original features contribute to the meta-features you end up with after using PCA? That’s a question that few people can answer although the answer is right there in front of them. Also, such a piece of information may be useful in evaluating the original features or providing some explanation of how much they are worth in terms of predictive potential, after the meta-features are used in a model.
All of these issues and more can be tackled by using a new approach to dimensionality reduction, one that is based on a new paradigm (the same one that can tackled the clustering issues mentioned in the previous post). Also, even though the new approach doesn’t use a network architecture, it can still be considered a type of A.I. as there is some kind of optimization involved. As for the specifics of the new approach, that’s something to be discussed in another post, when the time is right...
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Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.