principal component analysis

Kernel Principal Component Analysis (KPCA)

Kernel Principal Component Analysis (KPCA) is a non-linear dimensionality reduction technique. It is an extension of Principal Component Analysis (PCA) - which is a linear dimensionality reduction technique - using kernel methods.

machine learning

Principle of Sammon Mapping

Sammon mapping (also known as Sammon projection) is an algorithm that maps a high dimensional data to lower dimensional data by preserving the structure of inter point distances in the original data. Learn why Sammon Mapping is better than Principal Component Analysis (PCA)

machine learning

Why Principal Component Analysis (PCA) works?

We have demonstrated how and why Principal Component Analysis (PCA) works using the intuition behind the common operations used in the algorithm such as Variance, Covariance, Eigenvectors and Eigenvalues. Eigenvectors represent directions while Eigenvalues represent magnitude the importance

machine learning

Algorithm of Principal Component Analysis (PCA)

The algorithm of Principal Component Analysis (PCA) is based on a few mathematical ideas namely Variance, Convariance, Eigen Vectors and Eigen values. The algorithm is of eight simple steps including preparing the data set, calculating the covariance matrix, eigen vectors and values, new feature set

machine learning

Basic Ideas of Principal component analysis

Principal component analysis (PCA) is a technique to bring out strong patterns in a dataset by supressing variations. It is used to clean data sets to make it easy to explore and analyse. We have demonstrated an example of 17 dimensions and given the basic intuition of PCA