Browsing All posts tagged under »dictionary learning«

K-Means for dictionary learning

July 10, 2011


One of the simplest, and yet most heavily constrained form of matrix factorization, is vector quantization (VQ). Heavily used in image/video compression, the VQ problem is a factorization where (our dictionary) is called the codebook and is designed to cover the cloud of data points effectively, and each line of is a unit vector. This […]

Image denoising with dictionary learning

July 7, 2011


I am presenting an image denoising example that fully runs under my local scikits-learn fork. Coming soon near you! The 400 square pixels area covering Lena’s face was distorted by additive gaussian noise with a standard deviation of 50 (pixel values are ranged 0-256.) The dictionary contains 100 atoms of shape 4×4 and was trained […]

Sparse PCA

May 23, 2011


I have been working on the integration into the scikits.learn codebase of a sparse principal components analysis (SparsePCA) algorithm coded by Gaël and Alexandre and based on [1]. Because the name “sparse PCA” has some inherent ambiguity, I will describe in greater depth what problem we are actually solving, and what it can be used for. […]