TY - CONF
AB - We describe an algorithm that performs regularized non-negative matrix factorization (NMF) to find independent components in non-negative data. Previous techniques proposed for this purpose require the data to be grounded, with support that goes down to 0 along each dimension. In our work, this requirement is eliminated. Based on it, we present a technique to find a low-dimensional decomposition of spectrograms by casting it as a problem of discovering independent non-negative components from it. The algorithm itself is implemented as regularized non-negative matrix factorization (NMF). Unlike other ICA algorithms, this algorithm computes the mixing matrix rather than an unmixing matrix. This algorithm provides a better decomposition than standard NMF when the underlying sources are independent. It makes better use of additional observation streams than previous non-negative ICA algorithms.
AU - Raj, Bhiksha
AU - Wilson, Kevin W.
AU - Krueger, Alexander
AU - Haeb-Umbach, Reinhold
ID - 11887
T2 - Interspeech 2010
TI - Ungrounded Independent Non-Negative Factor Analysis
ER -