For efficient processing of similarity queries, the search space is often reduced by pruning inactive query subspaces which do not contain any query results so only those active query subspaces which may contain query results are examined. Among the active query subspaces, however, not all of them contain query results; an active query subspace that later turns out to contain no query results a...

In this paper, we propose a novel shape representation we call Directional Histogram Model (DHM). It captures the shape variation of an object and is invariant to scaling and rigid transforms. The DHM is computed by first extracting a directional distribution of thickness histogram signatures, which are translation invariant. We show how the extraction of the thickness histogram distribution ca...

For one-dimensional growth processes we consider the distribution of the height above a given point of the substrate and study its scale invariance in the limit of large times. We argue that for self-similar growth from a single seed the universal distribution is the Tracy-Widom distribution from the theory of random matrices and that for growth from a flat substrate it is some other, only nume...

A set of ordinal assumptions, formulated in terms of a given multidimensional stimulus set, is shown to yield essentially unique additive difference measurement of dissimilarity, or psychological distance. According to this model, dissimilarity judgments between multidimensional objects are regarded as composed of two independent processes: an intradimensional subtractive process, and an interd...

Nearest neighbor search has a wide variety of applications. Unfortunately, the majority of search methods do not scale well with dimensionality. Recent efforts have been focused on finding better approximate solutions that improve the locality of data using dimensionality reduction. However, it is possible to preserve the locality of data and find exact nearest neighbors in high dimensions with...

Multidimensional similarity join finds pairs of multidimensional points that are within some small distance of each other. The -k-d-B tree has been proposed as a data structure that scales better as the number of dimensions increases compared to previous data structures. We present a cost model of the -k-d-B tree and use it to optimize the leaf

Group theoretic methods are used to construct new exact solutions for the one-dimensional Fokker-Planck equation corresponding to a class of non-linear forcing functions f(.‘cl. An important sub-class, corresponding tof(x) = a/x + /I-Y, a < 1, /? > 0 is shown to lead to stable solutions. A discussion is given on how generalized similarity methods could be applied to higher dimensional systems. ...

The rise of internet has resulted in an explosion of data consisting of millions of articles, images, songs, and videos. Most of this data is high dimensional and sparse. The need to perform an efficient search for similar objects in such high dimensional big datasets is becoming increasingly common. Even with the rapid growth in computing power, the bruteforce search for such a task is impract...

Recent research efforts attempt to combine multiple feature selection techniques instead of using a single one. However, this combination is often made on an “ad hoc” basis, depending on the specific problem at hand, without considering the degree of diversity/similarity of the involved methods. Moreover, though it is recognized that different techniques may return quite dissimilar outputs, esp...

In this paper, we propose a fully decentralized framework called iDISQUE to support tunable approximate similarity query of high dimensional data in DHT networks. The iDISQUE framework utilizes a distributed indexing scheme to organize data summary structures called iDisques, which describe the cluster information of the data on each peer. The publishing process of iDisques employs a locality-p...