We consider matching functions in vector quantization (VQ) based speaker recognition systems. In VQ-based systems, a speaker model consists of a small collection of representative vectors, and matching is performed by computing a dissimilarity value between the unknown speaker’s feature vectors and the speaker models. Typically, the average/total quantization error is used as the dissimilarity ...

Many content-based recommendation approaches are based on a dissimilarity measure based on the product attributes. In this paper, we evaluate four dissimilarity measures for product recommendation using an online survey. In this survey, we asked users to specify which products they considered to be relevant recommendations given a reference product. We used microwave ovens as product category. ...

In sound compression and synthesis, it is valuable to have objective distance measures which can predict the perceived dissimilarity of two sounds. Furthermore it is desirable also to be able to estimate the extent of the dissimilarity or the quality difference between sounds. Since it is the perceived difference that needs to be quantified it can be expected that measures that are derived from...

Dynamic Time Warping is a well-known measure of dissimilarity between time series. Due to its flexibility to deal with non-linear distortions along the time axis, this measure has been widely utilized in machine learning models for this particular kind of data. Nowadays, the proliferation of streaming data sources has ignited the interest and attention of the scientific community around on-line...

Extreme Learning Machine (ELM) has salient features such as fast learning speed and excellent generalization performance. However, a single extreme learning machine is unstable in data classification. To overcome this drawback, more and more researchers consider using ensemble of ELMs. This paper proposes a method integrating voting-based extreme learning machines (V-ELM) with dissimilarity (D-...

Nearest neighbour search is a simple technique widely used in Pattern Recognition tasks. When the dataset is large and/or the dissimilarity computation is very time consuming the brute force approach is not practical. In such cases, some properties of the dissimilarity measure can be exploited in order to speed up the search. In particular, the metric properties of some dissimilarity measures h...

Classical multidimensional scaling constructs a configuration of points that minimizes a certain measure of discrepancy between the configuration’s interpoint distance matrix and a fixed dissimilarity matrix. Recent extensions have replaced the fixed dissimilarity matrix with a closed and convex set of dissimilarity matrices. These extensions lead to optimization problems with two sets of decis...

In this work we explore the dissimilarity between symmetric word pairs, by comparing the inter-word distance distribution of a word to that of its reversed complement. We propose a new measure of dissimilarity between such distributions. Since symmetric pairs with different patterns could point to evolutionary features, we search for the pairs with the most dissimilar behaviour. We focus our st...

The usefulness and the efficiency of the k nearest neighbor classification procedure are well known. A less sophisticated method consists in using only the first nearby prototype. This means k=1 and it is the method applied in this paper. One way to get a proper result is to use weighted dissimilarities implemented with a distance function of the prototype space. To improve the classification a...

Histogram-based dissimilarity measures are extensively used for content-based image retrieval. In an earlier paper [1], we proposed an efficient weighted correlation dissimilarity measure for adaptive-binning color histograms. Compared to existing fixed-binning histograms and dissimilarity measures, adaptive histograms together with weighted correlation produce the best overall performance in t...