NeurIPS- Learning the Dependency Structure of Latent Factors

1 minute read

Paper: @NeurIPS12

  • Yunlong He, Yanjun Qi, Koray Kavukcuoglu, Haesun Park


Poster PDF


In this paper, we study latent factor models with the dependency structure in the latent space. We propose a general learning framework which induces sparsity on the undirected graphical model imposed on the vector of latent factors. A novel latent factor model SLFA is then proposed as a matrix factorization problem with a special regularization term that encourages collaborative reconstruction. The main benefit (novelty) of the model is that we can simultaneously learn the lower-dimensional representation for data and model the pairwise relationships between latent factors explicitly. An on-line learning algorithm is devised to make the model feasible for 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 our model provide a more structured way of exploring high-dimensional data, and the learned representations achieve the state-of-the-art classification performance.





  title={Learning the dependency structure of latent factors},
  author={He, Yunlong and Qi, Yanjun and Kavukcuoglu, Koray and Park, Haesun},
  booktitle={Advances in neural information processing systems},

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