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 sε K : p ( s ⁢  μ , Θ ) = 1 Z ⁡ ( μ , Θ ) ⁢ exp ⁡ ( - ∑ i = 1 K ⁢ μ i ⁢ s i - 1 2 ⁢ ∑ i = 1 K ⁢ ∑ j = 1 K ⁢ θ ij ⁢ s i ⁢ s j ) ( 4 ) with parameter μ=[μi], symmetric Θ=[θ ij ], 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.
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 sε K : p ( s ⁢  μ , Θ ) = 1 Z ⁡ ( μ , Θ ) ⁢ exp ⁡ ( - ∑ i = 1 K ⁢ μ i ⁢ s i - 1 2 ⁢ ∑ i = 1 K ⁢ ∑ j = 1 K ⁢ θ ij ⁢ s i ⁢ s j ) ( 4 ) with parameter μ=[μi], symmetric Θ=[θ ij ], 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. 2. The computer implemented method of claim 1 wherein said model identifies a dependency structure in the latent space.