In recent years, metric learning in the semisupervised setting has aroused a lot of research interests. One type of semi-supervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale up well with the data set size. In this paper, we...

This paper describes a semi-supervised distance metric learning algorithm which uses pairwise equivalence (similarity and dissimilarity) constraints to discover the desired groups within high-dimensional data. As opposed to the traditional full rank distance metric learning algorithms, the proposed method can learn nonsquare projection matrices that yield low rank distance metrics. This brings ...

This paper describes a method of learning similarity matrix from pairwise constraints assumed used under the situation such as interactive clustering, where we can expect little user feedback. With the small number of pairwise constraints used, our method attempts to use additional constraints induced by the affinity relationship between constrained data and their neighbors. The similarity matr...

Although the “scale-free” literature is large and growing, it gives neither a precise definition of scale-free graphs nor rigorous proofs of many of their claimed properties. In fact, it is easily shown that the existing theory has many inherent contradictions and verifiably false claims. In this paper, we propose a new, mathematically precise, and structural definition of the extent to which a...

Dimensionality reduction is one of the key processes of high dimensional data analysis, including machine learning and pattern recognition. Constrained Locality Preserving Projections (CLPP) is a variant of Locality Preserving Projections (LPP) plus with pairwise constraints and constraints propagation. Like LPP, however, CLPP is still sensitive to noise and parameters. To overcome these proble...

We introduce a new algorithm for distance metric learning which uses pairwise similarity (equivalence) and dissimilarity constraints. The method is adapted to the high-dimensional feature spaces that occur in many computer vision applications. It first projects the data onto the subspace orthogonal to the linear span of the difference vectors of the similar sample pairs. Similar samples thus ha...

The pairwise dissimilarities of a set of items can be intuitively visualized by a 2D arrangement of the items, in which the distances reflect the dissimilarities. Such an arrangement can be obtained by multidimensional scaling (MDS). We propose a method for the inverse process: inferring the pairwise dissimilarities from multiple 2D arrangements of items. Perceptual dissimilarities are classica...

Conditions under which pairwise dissimilarity ratings should reflect manipulations of the stimulus distribution were outlined by a model that proposed these effects. These conditions arise from either a context dependent process for constructing implicit scale values or a process that uses previously established stimulus-response associates. Consistent with the model, results from 3 experiments...

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...

Homologous proteins are often compared by pairwise sequence alignment, and structure superposition if the atomic coordinates available. Unification of data is an important task in structural biology. Here, we present Sequence Similarity 3D (SS3D) method integrating information. SS3D a distance substitution matrix-based for straightforward visualization regions similarity difference between homo...