As you can see, not all points lie on this plane, but we can say that they approximately do. This plane is two-dimensional, so it is defined by two variables. In some cases, we can find a 2D plane very close to the data. Now, in order to represent each of those points, we have used 3 values – one for each dimension. Imagine we have a dataset with 3-variables. The first question of the day is: What Is Dimensionality Reduction? By the end of the article, you’ll be able to perform a Principal Component Analysis yourself. ![]() We’ll talk about Principal Component Analysis definition, its practical application, and how to interpret PCA. So, in this article, we’ll take a close look at dimensionality reduction and Principal Components Analysis. In order to avoid the curse of dimensionality one can employ dimensionality reduction. ![]() The curse of dimensionality isn’t the title of an unpublished Harry Potter manuscript but is what happens if your data has too many features and possibly not enough data points. ![]() Principal Components Analysis or PCA is a popular dimensionality reduction technique you can use to avoid “the curse of dimensionality”.īut what is the curse of dimensionality? And how can we escape it?
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