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» The intrinsic dimensionality of graphs
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EDBT
2009
ACM
277views Database» more  EDBT 2009»
14 years 2 months ago
G-hash: towards fast kernel-based similarity search in large graph databases
Structured data including sets, sequences, trees and graphs, pose significant challenges to fundamental aspects of data management such as efficient storage, indexing, and simila...
Xiaohong Wang, Aaron M. Smalter, Jun Huan, Gerald ...
NIPS
2008
13 years 11 months ago
Dimensionality Reduction for Data in Multiple Feature Representations
In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. These representa...
Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
AAIM
2007
Springer
118views Algorithms» more  AAIM 2007»
14 years 1 months ago
Significance-Driven Graph Clustering
Abstract. Modularity, the recently defined quality measure for clusterings, has attained instant popularity in the fields of social and natural sciences. We revisit the rationale b...
Marco Gaertler, Robert Görke, Dorothea Wagner
IJKDB
2010
162views more  IJKDB 2010»
13 years 7 months ago
New Trends in Graph Mining: Structural and Node-Colored Network Motifs
Searching for repeated features characterizing biological data is fundamental in computational biology. When biological networks are under analysis, the presence of repeated modul...
Francesco Bruno, Luigi Palopoli, Simona E. Rombo
DAGM
2006
Springer
14 years 1 months ago
Parameterless Isomap with Adaptive Neighborhood Selection
Abstract. Isomap is a highly popular manifold learning and dimensionality reduction technique that effectively performs multidimensional scaling on estimates of geodesic distances....
Nathan Mekuz, John K. Tsotsos