Sparse semi-supervised heterogeneous interbattery bayesian analysis

Semi-supervised learning with sparse grids

Sparse grids were recently introduced for classification and regression problems. In this article we apply the sparse grid approach to semi-supervised classification. We formulate the semi-supervised learning problem by a regularization approach. Here, besides a regression formulation for the labeled data, an additional term is involved which is based on the graph Laplacian for an adjacency gra...

Semi-Supervised Learning with Sparse Distributed Representations

For many machine learning applications, labeled data may be very difficult or costly to obtain. For instance in the case of speech analysis, the average annotation time for a one hour telephone conversation transcript is 400 hours.[7] To circumvent this problem, one can use semi-supervised learning algorithms which utilize unlabeled data to improve performance on a supervised learning task. Sin...

Sparse Semi-supervised Learning Using Conjugate Functions

In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent target functions and thus has the merit of accelerating function evaluations when predicting the output of a new example. This framework makes use of Fenchel-Legendre conjugates to rewrite a convex insensitive loss involvin...

Semi-supervised Learning by Sparse Representation

In this paper, we present a novel semi-supervised learning framework based on `1 graph. The `1 graph is motivated by that each datum can be reconstructed by the sparse linear superposition of the training data. The sparse reconstruction coefficients, used to deduce the weights of the directed `1 graph, are derived by solving an `1 optimization problem on sparse representation. Different from co...

Semi-supervised Clustering Using Heterogeneous Dissimilarities