Efficient similarity search and classification via rank aggregation

By Ronald Fagin, Ravi Kumar, D. Sivakumar. 2003

457 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Rank Aggregation: Together We're Strong (2009, background): “Fagin et al [10, 11, 12] proposed a set of different distance measures between two partial rankings and a permutation and a partial ranking.”
Handbook of Computational Social Choice (2016, methodology): “Here we briefly describe an application to similarity search and classification for high-dimensional data (Fagin et al., 2003).”
Aggregating inconsistent information: ranking and clustering (2005, background): “This problem dates back to as early as the late 18th century when Condorcet and Borda each proposed voting systems for elections with more than two candidates [11, 8]. There are numerous applications in sports, databases, and statistics [14, 17 ] in which it is necessary to eectiv ely combine ranki”
Score Aggregation Techniques in Retrieval Experimentation (2009, methodology): “To broaden the scope of our investigation we also plan to explore both further aggregation mechanisms, such as the MEDRANK approach of Fagin et al. (2003); and also other rank correlation approaches, including theτAP topweighted approach of Yilmaz et al. (2008).”
Efficient similarity joins for near duplicate detection (2008, methodology): “The near duplicate object detection problem is also a generalization of the well-known nearest neighbor problem, which is studied by a wide body of work, with many approximation techniques considered by recent work [7, 13, 16, 21].”
Permutation Search Methods are Efficient, Yet Faster Search is Possible (2015, methodology): “[20], indexes based on the inverted files are database friendly, because they require neither complex data structures nor many random accesses.”
A contextual dissimilarity measure for accurate and efficient image search (2007, background): “There is a large body of approximate search techniques [1, 3, 5].”