People categorized pairs of perceptual stimuli that varied in both category membership and pairwise similarity. Experiments 1 and 2 showed categorization of 1 color of a pair to be reliably contrasted from that of the other. This similarity-based contrast effect occurred only when the context stimulus was relevant for the categorization of the target (Experiment 3). The effect was not simply ow...

It is a challenging problem to efficiently interpret the large volumes of remotely sensed image data being collected in the current age of remote sensing “big data”. Although human visual interpretation can yield accurate annotation of remote sensing images, it demands considerable expert knowledge and is always time-consuming, which strongly hinders its efficiency. Alternatively, intelligent a...

Networks, in which nodes represent compounds and edges pairwise similarity relationships, are used as coordinate-free representations of chemical space. So-called chemical space networks (CSNs) provide intuitive access to structural relationships within compound data sets and can be annotated with activity information. However, in such similarity-based networks, distances between compounds are ...

Recently a new clustering algorithm was developed, useful in phylogenetic systematics and taxonomy. It derives a hierarchy from (dis)similarity data on a simple and rather natural way. It transforms a given dissimilarity by an iterative approach. Each iteration step consists of ranking the objects under consideration according to their pairwise dissimilarity and calculating the Euclidian distan...

This paper presents a novel neural network model, called Similarity Neural Network (SNN), designed to learn similarity measures for pairs of patterns exploiting binary supervision. The model guarantees to compute a non negative and symmetric measure, and shows good generalization capabilities even if a small set of supervised examples is used for training. The approximation capabilities of the ...

For complex data sets, the pairwise similarity or dissimilarity of data often serves as the interface of the application scenario to the machine learning tool. Hence, the final result of training is severely influenced by the choice of the dissimilarity measure. While dissimilarity measures for supervised settings can eventually be compared by the classification error, the situation is less cle...

We present a probabilistic framework for learning pairwise similarities between objects belonging to different modalities, such as drugs and proteins, or text and images. Our framework is based on learning a binary code based representation for objects in each modality, and has the following key properties: (i) it can leverage both pairwise as well as easy-to-obtain relative preference based cr...

Visualizing and structuring pairwise dissimilarity data are difficult combinatorial optimization problems known as multidimensional scaling or pairwise data clustering. Algorithms for embedding dissimilarity data set in a Euclidian space, for clustering these data and for actively selecting data to support the clustering process are discussed in the maximum entropy framework. Active data select...

The objective of the present study is to identify similarity and dissimilarity in the perception of the taxpayers regarding the returns and assessment aspects under VAT in Assam and to locate the issues of similarity and differences in the perception. The study is based on the primary data collected from the taxpayers of Tinsukia town of Assam by the means of questionnaire. It is found that the...