Autori: Laszlo Kozma, Tapani Raiko, Alexander Ilin

Editorial: IEEE, Machine Learning for Signal Processing (MLSP), 2009.

Rezumat:

We propose an algorithm for binary principal component
analysis (PCA) that scales well to very high dimensional
and very sparse data. Binary PCA finds components from
data assuming Bernoulli distributions for the observations.
The probabilistic approach allows for straightforward treatment
of missing values. An example application is collaborative
filtering using the Netflix data. The results are comparable
with those reported for single methods in the literature
and through blending we are able to improve our previously
obtained best result with PCA.

Cuvinte cheie: sisteme de recomandare, factorizare // binary pca, pca, principal components, netflix, collaborative filtering

URL: http://lkozma.net/mlsp09binary.pdf