Contents
What do you need to know about PCA algorithms?
Often, people end up making a mistake in thinking that PCA selects some features out of the dataset and discards others. The algorithm actually constructs new set of properties based on combination of the old ones.
How does principal component analysis ( PCA ) improve classification?
One of the advantages pointed out by authors is that these algorithms can improve the results of classification task. In this post, I am going to veri f y this statement using a Principal Component Analysis ( PCA ) to try to improve the classification performance of a neural network over a dataset.
How is PCA used to build principal component space?
On the other hand, PCA looks for properties that show as much variation across classes as possible to build the principal component space. The algorithm use the concepts of variance matrix, covariance matrix, eigenvector and eigenvalues pairs to perform PCA, providing a set of eigenvectors and its respectively eigenvalues as a result.
Which is the PCA function in descending order?
In a practical way, you will see that PCA function from R package provides a set of eigenvalues already sorted in descending order, it means that the first component is that one with the highest variance, the second component is the eigenvector with the second highest variance and so on.
What is the accuracy of the classifier in PCA?
In this first result, the classifier shows itself very confused, because it classified correctly almost all examples from “saab” class, but it also classified most examples of “bus” class as “saab” class. Reinforcing this results, we can see that the value of accuracy is around 50%, it is a really bad result for classification task.
How is PCA used to find variance in data?
PCA is a method to find variance in the data. It transforms the coordinate system such that the data has the highest variance along the first component, the second highest along the second component,
How can PCA improve the result of classification task?
Let’s see if PCA really improves the result of classification task. In order to comprove it, my strategy is to apply a neural network over a dataset and see its initial results. Afterwards, I am going to perform PCA before classification and apply the same neural network over the new dataset and last compare both results.