A quadratic lower bound for rocchio's similarity-based relevance feedback algorithm

Authors:
Zhixiang Chen;Bin Fu
Affiliations:
Department of Computer Science, University of Texas-Pan American, Edinburg, TX;Department of Computer Science, University of New Orleans, New Orleans, LA
Venue:
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
Year:
2005

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Abstract

It is shown in [4] that Rocchio’s similarity-based relevance feedback algorithm makes Ω(n) mistakes in searching for a collection of documents represented by a monotone disjunction of at most k relevant features (or terms) over the n-dimensional binary vector space {0, 1}n. In practice, Rocchio’s algorithm often uses a fixed query updating factor and a fixed classification threshold. When this is the case, we strengthen the work in [4] in this paper and prove that Rocchio’s algorithm makes Ω(k(n–k)) mistakes in searching for the same collection of documents over the binary vector space {0, 1}n. A quadratic lower bound is obtained when k is proportional to n. An O(k(n–k)2) upper bound is also obtained.