We propose a novel approach for solving the approximate nearest neighbor search problem in arbitrary metric spaces. The distinctive feature of our approach is that we can incrementally build a non-hierarchical distributed structure for given metric space data with a logarithmic complexity scaling on the size of the structure and adjustable accuracy probabilistic nearest neighbor queries. The st...

A Fundamental activity in vector quantization involves searching a set of n kdimensional data to find the nearest one. We present a fast algorithm that is full-search equivalent, i.e. the match is as good as the one that could be found using exhaustive search. The proposed method utilizes law of cosines to calculate an estimate for distance, which is used to reduce the search area. Experiments ...

Missing data is an important issue in almost all fields of quantitative research. A nonparametric procedure that has been shown to be useful is the nearest neighbor imputation method. We suggest a weighted nearest neighbor imputation method based on Lq-distances. The weighted method is shown to have smaller imputation error than available NN estimates. In addition we consider weighted neighbor ...

Important task when trying to find patterns in applications involving mining different types of data such as images, video, time series, text documents, DNA sequences, etc. Similarity searching module is a central component of content-based retrieval in multimedia databases Problem: finding objects in a data set S that are similar to a query object q based on some distance measure d which is us...

the proliferation of multimedia data, there is an increasing need to support the indexing and searching of high-dimensional data. In this paper, we propose an efficient indexing method for high-dimensional multimedia databases using the filtering approach, known also as vector approximation approach which supports the nearest neighbor search efficiently. Our technique called RA +-Blocks (Region...

The k-Nearest Neighbor (k-NN) classification algorithm is one of the most widely-used lazy classifiers because of its simplicity and ease of implementation. It is considered to be an effective classifier and has many applications. However, its major drawback is that when sequential search is used to find the neighbors, it involves high computational cost. Speeding-up k-NN search is still an act...

We introduce a new nearest neighbor search algorithm. The algorithm builds a nearest neighbor graph in an offline phase and when queried with a new point, performs hill-climbing starting from a randomly sampled node of the graph. We provide theoretical guarantees for the accuracy and the computational complexity and empirically show the effectiveness of this algorithm.

Fractal image encoding is a computationally intensive method of compression due to its need to find the best match between image subblocks by repeatedly searching a large virtual codebook constructed from the image under compression. One of the most innovative and promising approaches to speed up the encoding is to convert the range-domain block matching problem to a nearest neighbor search pro...

This paper examines the problem of database organization and retrieval based on computing metric pairwise distances. A low-dimensional Euclidean approximation of a high-dimensional metric space is not efficient, while search in a high-dimensional Euclidean space suffers from the “curse of dimensionality”. Thus, techniques designed for searching metric spaces must be used. We evaluate several su...

A technique for creating and searching a tree of patterns using relative distances is presented. The search is conducted to find patterns which are nearest neighbors of a given test pattern. The structure of the tree is such that the search time is proportional to the distance hetween the test pattern and its nearest neighbor, which suggests the anomalous possibility that a larger tree, which c...