43411240
Semi-Supervised Graph-Ranking for Text Retrieval
Much work has been done on supervised ranking for information retrieval, where the goal is to rank all searched documents in a known repository with many labeled query-document pairs. Unfortunately, the labeled pairs are lack because human labeling is often expensive, difficult and time consuming. To address this issue, we employ graph to represent pairwise relationships among the labeled and unlabeled documents, in order that the ranking score can be propagated to their neighbors. Our main contribution in this paper is to propose a semi-supervised ranking method based on graph-ranking and different weighting schemas. Experimental results show that our method called SSG-Rank on 20-newsgroups dataset outperforms supervised ranking (Ranking SVM and PRank) and unsupervised graph ranking significantly.
40027798
Multiple Ranker Method in Document Retrieval
In this paper, we propose a multiple-ranker approach to make learning to rank methods more effective for document retrieval application. In traditional learning to rank methods, a ranker is learned from a set of queries together with their corresponding document rankings labeled by experts, and it is then used to predict the document rankings for new queries. But in practice, user queries vary in large diversity, which makes the single ranker learned from a close set of data not representative. The single ranker cannot be guaranteed with the best ranking result for every single query, and this becomes the bottleneck of traditional learning to rank approaches. To address this problem, we propose a multi-ranker approach. We train multiple diverse rankers which can cover diverse categories of queries, instead of an isolated one, and take an ensemble of these rankers for final prediction. We verify the proposed multipleranker approach over real-world datasets. The experimental results indicate that the proposed approach can outperform existing ‘learning to rank’ methods significantly.
13971731
Video segmentation based on graphical models
This paper proposes a unified framework for spatiotemporal segmentation of video sequences. A Bayesian network is presented to model the interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notions of distance transformation and Markov random field are used to express spatiotemporal constraints. Given consecutive frames, an optimization method is proposed to maximize the conditional probability density of the three fields in an iterative way. Experimental results show that the approach is robust and generates spatiotemporally coherent segmentation results.
1878790
Unsupervised Discovery of Action Classes
In this paper we consider the problem of describing the action being performed by human figures in still images. We will attack this problem using an unsupervised learning approach, attempting to discover the set of action classes present in a large collection of training images. These action classes will then be used to label test images. Our approach uses the coarse shape of the human figures to match pairs of images. The distance between a pair of images is computed using a linear programming relaxation technique. This is a computationally expensive process, and we employ a fast pruning method to enable its use on a large collection of images. Spectral clustering is then performed using the resulting distances. We present clustering and image labeling results on a variety of datasets.
7385162
On the use of high-order ambiguity function for multi-component polynomial phase signals
Abstract Nonstationary signals appear often in real-life applications and many of them can be modeled as polynomial-phase signals (PPS). High-order ambiguity function (HAF) was first introduced to estimate the parameters of a single-component PPS, but has not been widely used for multi-component PPS because of its nonlinearity. Multi-component PPS arise for example, in Doppler radar applications when multiple objects are tracked simultaneously. In this paper, we show that HAF is virtually additive for multi-component PPS and suggest an algorithm to estimate their parameters. Numerical examples are presented to illustrate the theories.
38323161
On validity of m-step multisplitting preconditioners for linear systems
Let Ax=b be a linear system where A is a symmetric positive definite (spd) matrix. m-step multisplitting preconditioners, which include the preconditioners based on multisplittings obtained by incomplete Cholesky factorizations [R. Bru, C. Corral, A. Martinez, J. Mas, SIAM J. Matrix Anal. Appl. 16 (1995) 1210-1222], for the conjugate gradient method are studied. The validity of the proposed m-step multisplitting preconditioners when A is an spd matrix is proved. Our results improve and extend previous ones.
16779458
Systems Biology Platform for Mechanism Clarification of TCM
According to the mechanism clarification problem of Traditional Chinese Medicine (TCM), a systems biology platform was constructed using the related achievements as the basis, Entity Grammar Systems as the framework and database construction, data mining, qualitative reasoning as the kernel techniques. This platform is composed of TCM active components database, TCM prescription database, drug targets database, biological molecular interaction network database, reasoning engine and user interface. After the TCM prescription is selected, the mechanism model can be generated automatically by the platform, which can be used to further study of TCM. This work will contribute to the modernization of Chinese Medicine.