In this paper, we present a manifold model for medical relation extraction.Our model is built upon a medical corpus containing 80M sentences (11 gigabyte text) and designed to accurately and efficiently detect the key medical relations that can facilitate clinical decision making.Our approach integrates domain specific parsing and typing systems, and can utilize labeled as well as unlabeled examples.To provide users with more flexibility, we also take label weight into consideration.Effectiveness of our model is demonstrated both theoretically with a proof to show that the solution is a closed-form solution and experimentally with positive results in experiments.

Many high-dimensional data sets that lie on a low-dimensional manifold exhibit nontrivial regularities at multiple scales. Most work in manifold learning ignores this multiscale structure. In this paper, we propose approaches to explore the deep structure of manifolds. The proposed approaches are based on the diffusion wavelets framework, data driven, and able to directly process directional neighborhood relationships without ad-hoc symmetrization. The proposed multiscale algorithms are evaluated using both synthetic and real-world data sets, and shown to outperform previous manifold learning methods.

We incorporate relevance feedback into a learning to rank framework by exploiting query-specific document similarities. Given a few judged feedback documents and many retrieved but unjudged documents for a query, we learn a function that adjusts the initial ranking score of each document. Scores are fit so that documents with similar term content get similar scores, and scores of judged documents are close to their labels. By such smoothing along the manifold of retrieved documents, we avoid overfitting, and can therefore learn a detailed query-specific scoring function with several dozen term weights.

This paper describes a novel framework to jointly learn data-dependent label and locality-preserving projections. Given a set of data instances from multiple classes, the proposed approach can automatically learn which classes are more similar to each other, and construct discriminative features using both labeled and unlabeled data to map similar classes to similar locations in a lower dimensional space. In contrast to linear discriminant analysis (LDA) and its variants, which can only return c − 1 features for a problem with c classes, the proposed approach can generate d features, where d is bounded only by the number of the input features. We describe and evaluate the new approach both theoretically and experimentally, and compare its performance with other state of the art methods. 1

In this paper we introduce a novel approach to manifold alignment, based on Procrustes analysis. Our approach differs from "semi-supervised alignment" in that it results in a mapping that is defined everywhere - when used with a suitable dimensionality reduction method - rather than just on the training data points. We describe and evaluate our approach both theoretically and experimentally, providing results showing useful knowledge transfer from one domain to another. Novel applications of our method including cross-lingual information retrieval and transfer learning in Markov decision processes are presented.

We present new, general-purpose kernels for protein structure analysis, and describe how to apply them to structural motif discovery and function classification. Experiments show that our new methods are faster than conventional techniques, are capable of finding structural motifs, and are very effective in function classification. In addition to strong cross-validation results, we found possible new oxidoreductases and cytochrome P450 reductases and a possible new structural motif in cytochrome P450 reductases.