Bayesian Inference With Linear Support Vector Machines – The goal of this paper is to devise a novel method for computing the posterior of Bayesian inference. Previous work based on the supervised learning model usually uses the latent-variable model (LVM) to learn the posterior of the data, a method that has been developed based on regression or Bayesian programming. In this work, to achieve the optimal posterior of the LVM, the underlying latent variable model is trained with a linear class model. In the LVM, the class model learns a linear conditional model such that the residual distribution of the latent data is consistent with the distribution (i.e., the residual models are robust to the latent data over the entire data). In this learning technique, the class model learns a regression model such that the residual distribution of the data is robust to the latent data over the entire data. As demonstrated in the experiments, the proposed proposed method significantly outperforms the LVM in terms of posterior and data similarity to the posterior. The model is capable of correctly predicting the data with the highest likelihood, as well as accurately predicting the residuals of the data with the best likelihood.
We present a method for recovering the local and sparse representations of data from a sparse signal under the generalization framework of Gaussian processes. To the best of our knowledge this is the first such solution for sparse signal recovery. We show the performance gains of our method, as well as the properties of the underlying sparse recovery theory as well as a method to obtain it as a sparse matrix solution.
Recovering Questionable Clause Representations from Question-Answer Data
Multi-Instance Dictionary Learning in the Matrix Space with Applications to Video Classification
Bayesian Inference With Linear Support Vector Machines
Fast Convergence Rate of Sparse Signal RecoveryWe present a method for recovering the local and sparse representations of data from a sparse signal under the generalization framework of Gaussian processes. To the best of our knowledge this is the first such solution for sparse signal recovery. We show the performance gains of our method, as well as the properties of the underlying sparse recovery theory as well as a method to obtain it as a sparse matrix solution.