7099358
THU TREC 2002: Novelty Track Experiments
This is the first time that Tsinghua University took part in TREC. In this year's novelty track, our basic idea is to find the key factor that help people find relevant and new information on a set of documents with noise. We paid attention to three points: 1. how to get full information from a short sentence; 2. how to complement hidden well-known knowledge to the sentences; 3. how to make the determination of duplication. Accordingly, expansion-based technologies are the key points. Studies of expansion technologies have been performed on three levels: efficient query expansion based on thesaurus and statistics, replacement-based document expansion, and term-expansion-related duplication elimination strategy based on overlapping measurement. Besides, two issues have been studied: finding key information in topics, and dynamic result selection. A new IR system has been developed for the task. In the system, four weighting strategies have been implemented: ltn.lnu, BM2500, FUB1, FUB2. It provides both similarity and overlapping measurements, based on term expansion. Comparisons can be made on sentence-to-sentence or sentence-to-pool level.
31340161
The Empirical Impact of the Nature of Novelty Detection
Novelty detection systems aim at reducing redundant documents or sentences from a list of documents chronologically ordered. In the task, sentences appearing later in the list with no new meanings are eliminated. In an accompanying paper, the nature of novelty detection was revealed – Novelty as a combination of the PO (partial overlap) and CO (complete overlap) relations, which can be treated as two classification tasks; theoretical impacts were given. This paper provides what the nature of the task mean empirically. One new method – selected pool – implementing the nature of the task gained improvements on TREC Novelty datasets. New evaluation criteria are given, which are natural from the viewpoint of the nature of novelty detection.
44859876
A primal-dual approximation algorithm for the survivable network design problem in hypergraphs
Given a hypergraph with nonnegative costs on hyperedges, and a weakly supermodular function r:2V → Z+, where V is the vertex set, we consider the problem of finding a minimum cost subset of hyperedges such that for every set S ⊆ V, there are at least r(S) hyperedges that have at least one but no all endpoints in S. This problem captures a hypergraph generalization of the survivable network design problem (SNDP), and also the element connectivity problem (ECP). We present a primal-dual algorithm with a performance guarantee of dmax+H (rmax), where dmax+ is the maximum degree of hyperedges of positive costs, rmax = maxs r(S), and H(k) = 1 + 1/2 +...+ 1/k. In particular, our result contains a 2H(rmax)-approximation algorithm for ECP, which gives an independent and complete proof for the result first obtained by Jain et al. (Proceedings of the SODA, 1999, p. 484-489).
35924921
Approximating the Minimum k-way Cut in a Graph via Minimum 3-way Cuts
For an edge weighted undirected graph G and an integer k > 2, a k-way cut is a set of edges whose removal leaves G with at least k components. We propose a simple approximation algorithm to the minimum k-way cut problem. It computes a nearly optimal k-way cut by using a set of minimum 3-way cuts. We show that the performance ratio of our algorithm is 2 − 3/k for an odd k and 2 − (3k − 4)/(k2 − k) for an even k. The running time is O(kmn3 log(n2/m)) where n and m are the numbers of vertices and edges respectively.
5957902
Qualitative and quantitative car tracking from a range image sequence
In this paper, we present a car tracking system which provides quantitative and qualitative motion estimates of the tracked car simultaneously from a moving observer. First, we construct three motion models (constant velocity, constant acceleration, and turning) to describe the qualitative motion of a moving car. Then the models are incorporated into the Extended Kalman Filters to perform quantitative tracking. Finally, we develop, an Extended Interacting Multiple Model (EIMM) algorithm to manage the switching between models and to output both qualitative and quantitative motion estimates of the tracked car. Accurate motion modeling and efficient model management result in a high performance tracking system. The experimental results on simulated and real data demonstrate that our tracking system is reliable and robust, and runs in real-time. The multiple motion representations make the system useful in various autonomous driving tasks.
14270828
A Strict Approach to Approximating Lognormal Sum Distributions
In this paper, the least squares (LS) approximation approach is applied to solve the approximation problem of a sum of lognormal random variables (RV). The least squares curve fitting technique is first used to obtain the approximated closed-form pdf of the sum RV. The second time use of the least squares curve fitting technique brings the explicit closed-form expressions of the coefficients as a function of the number of the summands and the dB spread of the summands. Simulation results show that the proposed approximation exhibits a good match with the simulation results in the interested range of the distributions of the summands. Furthermore, errors due to a mixed use of the sum RV in the domain of the original variable and the domain of the logarithm are pointed out and the corrected results are presented
17699590
Adapting K-Means Algorithm for Discovering Clusters in Subspaces
Subspace clustering is a challenging task in the field of data mining. Traditional distance measures fail to differentiate the furthest point from the nearest point in very high dimensional data space. To tackle the problem, we design minimal subspace distance which measures the similarity between two points in the subspace where they are nearest to each other. It can discover subspace clusters implicitly when measuring the similarities between points. We use the new similarity measure to improve traditional k-means algorithm for discovering clusters in subspaces. By clustering with low-dimensional minimal subspace distance first, the clusters in low-dimensional subspaces are detected. Then by gradually increasing the dimension of minimal subspace distance, the clusters get refined in higher dimensional subspaces. Our experiments on both synthetic data and real data show the effectiveness of the proposed similarity measure and algorithm.