On the triangle inequalities in fuzzy metric spaces

This paper presents level forms of the triangle inequalities in fuzzy metric spaces (X, d, L, R). To aid discussion, a fuzzy pre-metric condition is introduced. It is first pointed out that under the fuzzy pre-metric condition the first triangle inequality is always equivalent to its level form. The second triangle inequality is equivalent to one level form when R is right continuous, and to another level form also when further conditions are imposed on R. In a fuzzy metric space, the level form of the first triangle inequality and one of the level forms of the second triangle inequality are always valid. The other level form of the second triangle inequality holds for all but at most countable α ∈ [0, 1). Finally, a fixed point theorem for fuzzy metric spaces is derived as an application of the preceding results.     

区间值度量空间的紧性和仿紧性

给出了区间值度量空间的概念,根据一般拓扑学中紧性的相关定义及其等价条件,证明了由区间值度量诱导的拓扑具有的紧性及其一系列等价关系,讨论了该诱导的拓扑空间具有仿紧性。     

On the connectivity threshold for general uniform metric spaces

Let μ be a measure supported on a compact connected subset of an Euclidean space, which satisfies a uniform d-dimensional decay of the volume of balls of the type

(1)     

Discrete and continuous metric spaces

Translated from Kibernetika i Sistemnyi Analiz, No. 6, pp. 172–174, November–December, 1992.     

Searching in metric spaces by spatial approximation

We propose a new data structure to search in metric spaces. A metric space is formed by a collection of objects and a distance function defined among them which satisfies the triangle inequality. The goal is, given a set of objects and a query, retrieve those objects close enough to the query. The complexity measure is the number of distances computed to achieve this goal. Our data structure, called sa-tree (“spatial approximation tree”), is based on approaching the searched objects spatially, that is, getting closer and closer to them, rather than the classic divide-and-conquer approach of other data structures. We analyze our method and show that the number of distance evaluations to search among n objects is sublinear. We show experimentally that the sa-tree is the best existing technique when the metric space is hard to search or the query has low selectivity. These are the most important unsolved cases in real applications. As a practical advantage, our data structure is one of the few that does not need to tune parameters, which makes it appealing for use by non-experts. Edited by R. Sacks-Davis Received: 17 April 2001 / Accepted: 24 January 2002 / Published online: 14 May 2002     

Adapting metric indexes for searching in multi-metric spaces

An important research issue in multimedia databases is the retrieval of similar objects. For most applications in multimedia databases, an exact search is not meaningful. Thus, much effort has been devoted to develop efficient and effective similarity search techniques. A recent approach that has been shown to improve the effectiveness of similarity search in multimedia databases resorts to the usage of combinations of metrics (i.e., a search on a multi-metric space). In this approach, the desirable contribution (weight) of each metric is chosen at query time. It follows that standard metric indexes cannot be directly used to improve the efficiency of dynamically weighted queries, because they assume that there is only one fixed distance function at indexing and query time. This paper presents a methodology for adapting metric indexes to multi-metric indexes, that is, to support similarity queries with dynamic combinations of metric functions. The adapted indexes are built with a single distance function and store partial distances to estimate the dynamically weighed distances. We present two novel indexes for multimetric space indexing, which are the result of the application of the proposed methodology.     

Probabilistic enhancement of approximate indexing in metric spaces

Some approximate indexing schemes have been recently proposed in metric spaces which sort the objects in the database according to pseudo-scores. It is known that (1) some of them provide a very good trade-off between response time and accuracy, and (2) probability-based pseudo-scores can provide an optimal trade-off in range queries if the probabilities are correctly estimated. Based on these facts, we propose a probabilistic enhancement scheme which can be applied to any pseudo-score based scheme. Our scheme computes probability-based pseudo-scores using pseudo-scores obtained from a pseudo-score based scheme. In order to estimate the probability-based pseudo-scores, we use the object-specific parameters in logistic regression and learn the parameters using MAP (Maximum a Posteriori) estimation and the empirical Bayes method. We also propose a technique which speeds up learning the parameters using pseudo-scores. We applied our scheme to the two state-of-the-art schemes: the standard pivot-based scheme and the permutation-based scheme, and evaluated them using various kinds of datasets from the Metric Space Library. The results showed that our scheme outperformed the conventional schemes, with regard to both the number of distance computations and the CPU time, in all the datasets.     

DNA library screening, pooling design and unitary spaces

Pooling design is an important research topic in bio-informatics due to its wide applications in molecular biology, especially DNA library screening. In this paper, with unitary spaces over finite fields, we present two new constructions whose efficiency ratio, i.e., the ratio between the number of tests and the number of items, is smaller than some of the existing constructions.     

Some results on set-valued contractions in abstract metric spaces

In this paper, the concept of contractive set-valued maps in the frame of abstract metric spaces is studied and the existence of fixed points for such maps is guaranteed under certain conditions. Consequently, several known fixed point results are either generalized or extended, including the corresponding recent fixed point results of Wardowski [D. Wardowski, Endpoints and fixed points of set-valued contractions in cone metric spaces, Nonlinear Anal. 71 (2009) 512-516] as well as Klim and Wardowski [D. Klim, D. Wardowski, Dynamic process and fixed points of set-valued nonlinear contractions in cone metric spaces, Nonlinear Anal. 71 (2009) 5170-5175]. Examples are given to show that our results are distinct from the existing ones.     

Regularity of null-additive fuzzy measure on metric spaces

In this paper, we investigate the regularity of null-additive fuzzy measures on metric spaces and show that Lusin's theorem on fuzzy measure space remains valid for those fuzzy measures with null-additivity. Egoroff's theorem for fuzzy measures on metric spaces is presented. These are generalizations and improvements of the previous results obtained by others.     

A fast pivot-based indexing algorithm for metric spaces

This work focus on fast nearest neighbor (NN) search algorithms that can work in any metric space (not just the Euclidean distance) and where the distance computation is very time consuming. One of the most well known methods in this field is the AESA algorithm, used as baseline for performance measurement for over twenty years. The AESA works in two steps that repeats: first it searches a promising candidate to NN and computes its distance (approximation step), next it eliminates all the unsuitable NN candidates in view of the new information acquired in the previous calculation (elimination step).This work introduces the PiAESA algorithm. This algorithm improves the performance of the AESA algorithm by splitting the approximation criterion: on the first iterations, when there is not enough information to find good NN candidates, it uses a list of pivots (objects in the database) to obtain a cheap approximation of the distance function. Once a good approximation is obtained it switches to the AESA usual behavior. As the pivot list is built in preprocessing time, the run time of PiAESA is almost the same than the AESA one.In this work, we report experiments comparing with some competing methods. Our empirical results show that this new approach obtains a significant reduction of distance computations with no execution time penalty.     

Properties of embedding methods for similarity searching in metric spaces

Complex data types-such as images, documents, DNA sequences, etc.-are becoming increasingly important in modern database applications. A typical query in many of these applications seeks to find objects that are similar to some target object, where (dis)similarity is defined by some distance function. Often, the cost of evaluating the distance between two objects is very high. Thus, the number of distance evaluations should be kept at a minimum, while (ideally) maintaining the quality of the result. One way to approach this goal is to embed the data objects in a vector space so that the distances of the embedded objects approximates the actual distances. Thus, queries can be performed (for the most part) on the embedded objects. We are especially interested in examining the issue of whether or not the embedding methods will ensure that no relevant objects are left out. Particular attention is paid to the SparseMap, FastMap, and MetricMap embedding methods. SparseMap is a variant of Lipschitz embeddings, while FastMap and MetricMap are inspired by dimension reduction methods for Euclidean spaces. We show that, in general, none of these embedding methods guarantee that queries on the embedded objects have no false dismissals, while also demonstrating the limited cases in which the guarantee does hold. Moreover, we describe a variant of SparseMap that allows queries with no false dismissals. In addition, we show that with FastMap and MetricMap, the distances of the embedded objects can be much greater than the actual distances. This makes it impossible (or at least impractical) to modify FastMap and MetricMap to guarantee no false dismissals.     

Modeling and navigation of social information networks in metric spaces

We are living in a world of various kinds of social information networks with small-world and scale-free characteristics. It is still an intriguing problem for researchers to explain how and why so many obviously different networks emerge and share common intrinsic characteristics such as short diameter, higher cluster and power-law degree distribution. Most previous works studied the topology formation and information navigation of complex networks in separated models. In this paper, we propose a metric based range intersection model to explore the topology evolution and information navigation in a synthetic way. We model the network as a set of nodes in a distance metric space where each node has an ID and a range of neighbor information around its ID in the metric space. The range of a node can be seen as the local knowledge or information that the node has around its position in the metric space. The topology is formed by setting up a link between two nodes that have intersected ranges. Information navigation over the network is modeled as a greedy routing process using neighbor links and the distance metric. Different from previous models, we do not assume that nodes join the network one by one and set up link according to the degree distribution of existing nodes or distances between nodes. Range of node is the key factor determining the topology and navigation properties of a network. Moreover, as the ranges of nodes grow, the network evolves from a set of totally isolated nodes to a connected network. Thus, we can easily model the network evolutions in terms of the network size and the individual node information range using the range intersection model. A set of experiments shows that networks constructed using the range intersection model have the scale-free degree distribution, high cluster, short diameter, and high navigability properties that are owned by the real networks.