Learning quantifiable associations via principal sparse non-negative matrix factorization

Learning quantifiable associations via principal sparse non-negative matrix factorization

Association rules are traditionally designed to capture statistical relationship among itemsets in a given database. To additionally capture the quantitative association knowledge, Korn et.al. recently propose a paradigm named Ratio Rules [6] for quantifiable data mining. However, their approach is mainly based on Principle Component Analysis (PCA), and as a result, it cannot guarantee that the...

Group Sparse Non-negative Matrix Factorization for Multi-Manifold Learning

Many observable data sets such as images, videos and speech can be modeled by a mixture of manifolds which are the result of multiple factors (latent variables). In this paper, we propose a novel algorithm to learn multiple linear manifolds for face recognition, called Group Sparse Non-negative Matrix Factorization (GSNMF). Via the group sparsity constraint imposed on the column vectors of the ...

Sparse Learning via Maximum Margin Matrix Factorization

In this paper, an algorithm for sparse learning via Maximum Margin Matrix Factorization(MMMF) is proposed. The algorithm is based on L1 penality and Alternating Direction Method of Multipliers. It shows that with sparse factors, sparse factors method can obtain result as good as dense factors.

Non-negative Matrix Factorization via Archetypal Analysis

Given a collection of data points, non-negative matrix factorization (NMF) suggests to express them as convex combinations of a small set of ‘archetypes’ with non-negative entries. This decomposition is unique only if the true archetypes are non-negative and sufficiently sparse (or the weights are sufficiently sparse), a regime that is captured by the separability condition and its generalizati...

Non-negative matrix factorization