Incremental principal component analysis (IPCA) is typically used as a replacement for principal component analysis (PCA) when the dataset to be decomposed is too large to fit

Transform a signal as a sparse combination of Ricker wavelets. This example visually compares different sparse coding methods using the

These figures aid in illustrating how a point cloud can be very flat in one direction?which is where PCA comes in to choose a direction that is not flat.

An example of estimating sources from noisy data.

This example applies to The Olivetti faces dataset different unsupervised matrix decomposition (dimension reduction) methods from

Principal Component Analysis applied to the Iris dataset. See

An example comparing the effect of reconstructing noisy fragments of a raccoon face image using firstly online

The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 attributes: sepal length, sepal width,

This example shows that Kernel PCA is able to find a projection of the data that makes data linearly separable.

This example illustrates visually in the feature space a comparison by results using two different component analysis techniques.