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From R-bloggers:

“Curse of Dimensionality:

One of the most commonly faced problems while dealing with data analytics problems such as recommendation engines, text analytics is high-dimensional and sparse data. At many times, we face a situation where we have a large set of features and fewer data points, or we have data with very high feature vectors. In such scenarios, fitting a model to the dataset, results in lower predictive power of the model. This scenario is often termed as the curse of dimensionality. In general, adding more data points or decreasing the feature space, also known as dimensionality reduction, often reduces the effects of the curse of dimensionality.

In this blog, we will discuss about principal component analysis, a popular dimensionality reduction technique. PCA is a useful statistical method that has found application in a variety of fields and is a common technique for finding patterns in data of high dimension.”

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