Latent factor dependency structure determination

摘要

Disclosed is a general learning framework for computer implementation that induces sparsity on the undirected graphical model imposed on the vector of latent factors. A latent factor model SLFA is disclosed as a matrix factorization problem with a special regularization term that encourages collaborative reconstruction. Advantageously, the model may simultaneously learn the lower-dimensional representation for data and model the pairwise relationships between latent factors explicitly. An on-line learning algorithm is disclosed to make the model amenable to large-scale learning problems. Experimental results on two synthetic data and two real-world data sets demonstrate that pairwise relationships and latent factors learned by the model provide a more structured way of exploring high-dimensional data, and the learned representations achieve the state-of-the-art classification performance.

主权项

1. A computer implemented method of structured latent factor analysis comprising:
by a computer: learning one or more hidden dependency structures of latent factors of a set of data; modeling pairwise relationships among them and determining structural relationships through the use of a sparse Gaussian graphical model; outputting an indication of the latent factor relationships; wherein said pairwise relationship modeling is performed according to the following pairwise Markov Random Field (MRF) prior on a vector of factors sεK:p(s⁢μ,Θ)=1Z⁡(μ,Θ)⁢exp⁡(-∑i=1K⁢μi⁢si-12⁢∑i=1K⁢∑j=1K⁢θij⁢si⁢sj)(4)  with parameter μ=[μi], symmetric Θ=[θij], and partition function Z(μ,Θ) which normalizes the distribution, wherein p is a probability of a field configuration of (s|μ, Θ), K is a number of latent factors, s is an element of natural parameter K, and i and j are non-zero variables; and modeling the pairwise interaction simultaneously with the learning one or more hidden dependency structures of latent factors of a set of data.