The performance of many supervised and unsupervised learning algorithms is very sensitive to the choice of an appropriate distance metric. Previous work in metric learning and adaptation has mostly been focused on classification tasks by making use of class label information. In standard clustering tasks, however, class label information is not available. In order to adapt the metric to improve...

Pairwise proximity data, given as similarity or dissimilarity matrix, can violate metricity. This occurs either due to noise, fallible estimates, or due to intrinsic non-metric features such as they arise from human judgments. So far the problem of non-metric pairwise data has been tackled by essentially omitting the negative eigenvalues or shifting the spectrum of the associated (pseudo-)covar...

Aims: The present paper deals with documentation of diversity and analysis ecological aspects soil microfungal flora Sanjay Gandhi National Park (SGNP).
 Study Design: study area was divided into five zones, defined over span three ranges 10% peripheral area.
 Place Duration Study: SGNP, Maharashtra, India between September 2016 2020.
 Methodology: A total 43 samples were collect...

Optimization is often the computational bottleneck in disciplines such as statistics, biology, physics, finance or economics. Many optimization problems can be directly cast in the wellstudied convex optimization framework. For non-convex problems, it is often possible to derive convex or spectral relaxations, i.e., derive approximations schemes using spectral or convex optimization tools. Conv...

Question To better understand the influence of deep-time diversification on extant plant communities, we assessed how community dissimilarity increases with spatial and climatic distances at multiple taxonomic ranks (species, genus, family, order) in angiosperm trees. We tested prediction that dissimilarity–distance relationship should change across depending different biogeographical regions r...

Strings are ubiquitous in computer systems and hence string processing has attracted extensive research effort from computer scientists in diverse areas. One of the most important problems in string processing is to efficiently evaluate the similarity between two strings based on a specified similarity measure. String similarity search is a fundamental problem in information retrieval, database...

Investigations of similarity and opinion change seem to have inadvertently fostered the conclusion that any communicator-communicatee similarity will lead to opinion change, and that the resultant change is due directly to similarity and not to increased feelings of attractiveness for similar communicators. It was hypothesized and confirmed that, when communicator attractiveness is controlled, ...

this paper proposes a method for ranking decision making units (dmus) using some of the multiple criteria decision making / multiple attribute decision making          (mcdm /madm) techniques, namely, interval analytic hierarchy process (iahp)          and the technique for order preference by similarity to an ideal solution (topsis).          since the efficiency score of unity is assigned to ...

This paper aims at introducing a new dissimilarity measure for categorical objects into an extension of k-representative algorithm for clustering categorical data. Basically, the proposed dissimilarity measure is based on an information theoretic definition of similarity introduced by Lin [15] that considers the amount of information of two values in the domain set. In order to demonstrate the ...

Distance-based methods in pattern recognition and machine learning have to rely on a similarity or dissimilarity measure between patterns in the input space. For many applications, Euclidean distance in the input space is not a good choice and hence more complicated distance metrics have to be used. In this paper, we propose a parametric method for metric learning based on class label informati...