1,928 research outputs found
Incremental Sparse Bayesian Ordinal Regression
Ordinal Regression (OR) aims to model the ordering information between
different data categories, which is a crucial topic in multi-label learning. An
important class of approaches to OR models the problem as a linear combination
of basis functions that map features to a high dimensional non-linear space.
However, most of the basis function-based algorithms are time consuming. We
propose an incremental sparse Bayesian approach to OR tasks and introduce an
algorithm to sequentially learn the relevant basis functions in the ordinal
scenario. Our method, called Incremental Sparse Bayesian Ordinal Regression
(ISBOR), automatically optimizes the hyper-parameters via the type-II maximum
likelihood method. By exploiting fast marginal likelihood optimization, ISBOR
can avoid big matrix inverses, which is the main bottleneck in applying basis
function-based algorithms to OR tasks on large-scale datasets. We show that
ISBOR can make accurate predictions with parsimonious basis functions while
offering automatic estimates of the prediction uncertainty. Extensive
experiments on synthetic and real word datasets demonstrate the efficiency and
effectiveness of ISBOR compared to other basis function-based OR approaches
A Noise-Robust Fast Sparse Bayesian Learning Model
This paper utilizes the hierarchical model structure from the Bayesian Lasso
in the Sparse Bayesian Learning process to develop a new type of probabilistic
supervised learning approach. The hierarchical model structure in this Bayesian
framework is designed such that the priors do not only penalize the unnecessary
complexity of the model but will also be conditioned on the variance of the
random noise in the data. The hyperparameters in the model are estimated by the
Fast Marginal Likelihood Maximization algorithm which can achieve sparsity, low
computational cost and faster learning process. We compare our methodology with
two other popular learning models; the Relevance Vector Machine and the
Bayesian Lasso. We test our model on examples involving both simulated and
empirical data, and the results show that this approach has several performance
advantages, such as being fast, sparse and also robust to the variance in
random noise. In addition, our method can give out a more stable estimation of
variance of random error, compared with the other methods in the study.Comment: 15 page
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