dimensionality reduction

MachineX: The inevitable Principal Component Analysis

In this blog post, we will look at an interesting feature extraction technique of Machine Learning known as Principal Component Analysis (PCA). PCA is one of the powerful techniques in dimensionality reduction, in fact, the de facto standard for human face recognition. Let’s first understand what is dimensionality reduction Dimensionality Reduction As an example let’s say we have a data set with many-many features(which is Continue Reading

MachineX: The second dimensionality reduction method

In the previous blog we have gone through how more data or to be precise more dimensions in the data creates different problems like overfitting in classification and regression algorithms. This is known as “curse of dimensionality”. Then we have gone through the solutions to the problem i.e. dimensionality reduction. We were mainly focused on one of the dimensionality reduction method called feature selection. In this Continue Reading

MachineX: When data is a curse to learning

Data and learning are like best friends, perhaps learning is too dependent on data to be called as friends. When data overwhelms, learning acts pricey, so it feels more like a girlfriend-boyfriend sort of a relationship. Well don’t get confused or bothered on how I am comparing the data and learning, it is just my depiction of something called Dimensionality reduction in machine learning. On Continue Reading

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