Reverse nearest neighbor queries are useful in identifying objects that are of significant influence or importance. Existing methods either rely on pre-computation of nearest neighbor distances, do not scale well with high dimensionality, or do not produce exact solutions. In this work we motivate and investigate the problem of reverse nearest neighbor search on high dimensional, multimedia dat...

Nearest neighbor search (NNS) among large-scale and high-dimensional vectors has played an important role in recent large-scale multimedia search applications. This paper proposes an optimized codebook construction algorithm for approximate NNS based on product quantization. The proposed algorithm iteratively optimizes both codebooks for product quantization and an assignment table that indicat...

Approximate nearest neighbor search is a technique which greatly reduces processing time and required amount of memory. Generally, there are the relationships of trade-off among accuracy, processing time and memory amount. Therefore, analysis on the relationships is an important task for practical application of the approximate nearest neighbor search method. In this paper, we construct a model...

Similar document search is the problem of retrieving documents that resemble a given document. In this paper, we describe a cluster-based retrieval scheme that approximates the classic nearest neighbor search scheme, by identifying the clusters that are closest to the input document and restricting attention to these clusters only. Cluster signatures play an important role in the effectiveness ...

We investigate asymptotic approximations of two estimators of the conditional empirical process of Y given X= x, leading to a functional law of the iterated logarithm. Our main results show that the weak behavior of the conditional empirical processes at a given x is essentially the same as that of the empirical process, although the rates now depend on the bandwidth. We examine the kernel esti...

We consider the problem of approximate nearest neighbors in high dimensions, when the queries are lines. In this problem, given n points in R, we want to construct a data structure to support efficiently the following queries: given a line L, report the point p closest to L. This problem generalizes the more familiar nearest neighbor problem. From a practical perspective, lines, and low-dimensi...

Fast approximate nearest neighbor(NN) search in large databases is becoming popular. Several powerful learning-based formulations have been proposed recently. However, not much attention has been paid to a more fundamental question: how difficult is (approximate) nearest neighbor search in a given data set? And which data properties affect the difficulty of nearest neighbor search and how? This...

In this paper, we propose a nearest neighbour algorithm that uses the lower and upper approximations from fuzzy rough set theory in order to classify test objects, or predict their decision value. It is shown experimentally that our method outperforms other nearest neighbour approaches (classical, fuzzy and fuzzy-rough ones) and that it is competitive with leading classification and prediction ...

In th is paper we explore t h e problem of approximate nearest neighbor searches. W e propose a n i n d e x structure, t h e A N N t r e e (approximate nearest neighbor tree) t o solve this problem. T h e A N N t r e e supports high accuracy nearest neighbor search. T h e actual nearest neighbor of a query poin t can usually be f o u n d in the f i r s t leaf page accessed. T h e accuracy incre...

The k-NN graph has played a central role in increasingly popular data-driven techniques for various learning and vision tasks; yet, finding an efficient and effective way to construct k-NN graphs remains a challenge, especially for large-scale high-dimensional data. In this paper, we propose a new approach to construct approximate k-NN graphs with emphasis in: efficiency and accuracy. We hierar...