Principal Component Analysis (PCA) is a dimensionality reduction algorithm used to reduce the dimensionality of a dataset. Sparse PCA is one variation of PCA that can exploit the natural sparsity of data while extracting the principal components. In this article, I will introduce you to Sparse PCA in Machine Learning and its implementation using Python.
What is Sparse PCA?
Sparse PCA is a specialized variant of Principal Component Analysis (PCA) in machine learning that is used in statistical analysis, especially when analyzing multivariate data. It is used to reduce the dimensionality of a dataset by introducing sparsity structures in the input features.
Using the standard PCA, we can only select the most important midrange features, assuming each instance can be rebuilt using the same components. But by using the sparse method, we can use a limited number of components, but without the limitation given by a dense projection matrix. This can be done using a sparse matrix, where the number of non-zero elements is quite low.
Sparse PCA using Python
So, by using the power of the sparse method, we can solve many more dimensionality reduction problems more efficiently than a standard Principal Component Analysis method. Now let’s see how to implement this algorithm using Python. To implement it using Python, I will first import the necessary Python libraries and the dataset:
Now below is how you can implement Sparse PCA using Python to reduce the dimensionality of the dataset:
In the code above, I am implementing a SparsePCA method provided by the scikit-learn library in Python with 60 components. Here the amount of sparsity can be controlled using the alpha parameter, where higher alpha values lead to more sparse results.
Extracting sparse components is very useful whenever there is a need to rebuild each instance from a finite subset of features. Hope you liked this article on Sparse PCA in Machine Learning and its implementation using Python. Please feel free to ask your valuable questions in the comments section below.