42438893
Some Decomposition Invariants of Crowns
6644016
Multistar Decomposition of Complete Multigraphs
40521773
Cycle decompositions of crowns
Abstract For an integer n ⩾3, the crown S n 0 is defined to be the graph with vertex set { x 0 , x 1 ,…, x n −1 , y 0 , y 1 ,…, y n −1 } and edge set {x i y j : 0⩽i,j⩽n−1, i≠j} . In this paper we give some sufficient conditions for the edge decomposition of the crown into isomorphic cycles.
3682795
Multibody grouping via orthogonal subspace decomposition
Multibody structure from motion could be solved by the factorization approach. However, the noise measurements would make the segmentation difficult when analyzing the shape interaction matrix. This paper presents an orthogonal subspace decomposition and grouping technique to approach such a problem. We decompose the object shape spaces into signal subspaces and noise subspaces. We show that the signal subspaces of the object shape spaces are orthogonal to each other. Instead of using the shape interaction matrix contaminated by noise, we introduce the shape signal subspace distance matrix for shape space grouping. Outliers could be easily identified by this approach. The robustness of the proposed approach lies in the fact that the shape space decomposition alleviates the influence of noise, and has been verified with extensive experiments.
1116766
Leveraging Reusability: Cost-Effective Lexical Acquisition for Large-Scale Ontology Translation
Thesauri and ontologies provide important value in facilitating access to digital archives by representing underlying principles of organization. Translation of such resources into multiple languages is an important component for providing multilingual access. However, the specificity of vocabulary terms in most ontologies precludes fully-automated machine translation using general-domain lexical resources. In this paper, we present an efficient process for leveraging human translations when constructing domain-specific lexical resources. We evaluate the effectiveness of this process by producing a probabilistic phrase dictionary and translating a thesaurus of 56,000 concepts used to catalogue a large archive of oral histories. Our experiments demonstrate a cost-effective technique for accurate machine translation of large ontologies.
11169177
Mining maximal frequent intervals
Many real world data are associated with intervals of time or distance. Mining frequent intervals from such data allows the users to group transactions with similar behavior together. Previous work only focuses on the problem of mining frequent intervals in a discrete domain. This paper first proposes the notion of maximal frequent intervals, which are superior to frequent intervals in many perspectives, and then provides a method for mining maximal frequent intervals in either a discrete domain or a continuous domain. Experimental results indicate that our method outperforms previous method for mining frequent intervals, and improves the runtime by two orders of magnitude for databases with a large number of distinct endpoints.
6084733
A symbolic representation of time series, with implications for streaming algorithms
The parallel explosions of interest in streaming data, and data mining of time series have had surprisingly little intersection. This is in spite of the fact that time series data are typically streaming data. The main reason for this apparent paradox is the fact that the vast majority of work on streaming data explicitly assumes that the data is discrete, whereas the vast majority of time series data is real valued.Many researchers have also considered transforming real valued time series into symbolic representations, nothing that such representations would potentially allow researchers to avail of the wealth of data structures and algorithms from the text processing and bioinformatics communities, in addition to allowing formerly "batch-only" problems to be tackled by the streaming community. While many symbolic representations of time series have been introduced over the past decades, they all suffer from three fatal flaws. Firstly, the dimensionality of the symbolic representation is the same as the original data, and virtually all data mining algorithms scale poorly with dimensionality. Secondly, although distance measures can be defined on the symbolic approaches, these distance measures have little correlation with distance measures defined on the original time series. Finally, most of these symbolic approaches require one to have access to all the data, before creating the symbolic representation. This last feature explicitly thwarts efforts to use the representations with streaming algorithms.In this work we introduce a new symbolic representation of time series. Our representation is unique in that it allows dimensionality/numerosity reduction, and it also allows distance measures to be defined on the symbolic approach that lower bound corresponding distance measures defined on the original series. As we shall demonstrate, this latter feature is particularly exciting because it allows one to run certain data mining algorithms on the efficiently manipulated symbolic representation, while producing identical results to the algorithms that operate on the original data. Finally, our representation allows the real valued data to be converted in a streaming fashion, with only an infinitesimal time and space overhead.We will demonstrate the utility of our representation on the classic data mining tasks of clustering, classification, query by content and anomaly detection.
7550683
Will Pyramids Built of Nuggets Topple Over?
The present methodology for evaluating complex questions at TREC analyzes answers in terms of facts called "nuggets". The official F-score metric represents the harmonic mean between recall and precision at the nugget level. There is an implicit assumption that some facts are more important than others, which is implemented in a binary split between "vital" and "okay" nuggets. This distinction holds important implications for the TREC scoring model---essentially, systems only receive credit for retrieving vital nuggets---and is a source of evaluation instability. The upshot is that for many questions in the TREC testsets, the median score across all submitted runs is zero. In this work, we introduce a scoring model based on judgments from multiple assessors that captures a more refined notion of nugget importance. We demonstrate on TREC 2003, 2004, and 2005 data that our "nugget pyramids" address many shortcomings of the present methodology, while introducing only minimal additional overhead on the evaluation flow.