Label dissimilarity specifies that a pair of examples probably have different class labels. We present a semi-supervised classification algorithm that learns from dissimilarity and similarity information on labeled and unlabeled data. Our approach uses a novel graphbased encoding of dissimilarity that results in a convex problem, and can handle both binary and multiclass classification. Experim...

Distance metric learning is widely used in many visual computing methods, especially image classification. Among various metric learning approaches, Fisher Discriminant Analysis (FDA) is a classical metric learning approach utilizing the pair-wise semantic similarity and dissimilarity in image classification. Moreover, Local Fisher Discriminant Analysis (LFDA) takes advantage of local data stru...

We study the problem of classification when only a dissimilarity function between objects is accessible. That is, data samples are represented not by feature vectors but in terms of their pairwise dissimilarities. We establish sufficient conditions for dissimilarity functions to allow building accurate classifiers. The theory immediately suggests a learning paradigm: construct an ensemble of si...

Representational similarity analysis (RSA) tests models of brain computation by investigating how neural activity patterns reflect experimental conditions. Instead predicting directly, the predict geometry representation, as defined representational dissimilarity matrix (RDM), which captures similar or dissimilar different associated conditions are. RSA therefore first quantifies calculating a ...

For a specific set of features chosen for representing images, the performance of a content-based image retrieval (CBIR) system depends critically on the similarity or dissimilarity measure used. Instead of manually choosing a distance function in advance, a more promising approach is to learn a good distance function from data automatically. In this paper, we propose a kernel approach to impro...

Many supervised and unsupervised learning algorithms are very sensitive to the choice of an appropriate distance metric. While classification tasks can make use of class label information for metric learning, such information is generally unavailable in conventional clustering tasks. Some recent research sought to address a variant of the conventional clustering problem called semi-supervised c...

In this paper, we study the problem of comparing similarity and dissimilarity between two distinct vehicular trajectories by proposing an adjacency-based metric. This approach has a broad application in building truthfulness vehicles evaluating paths hazardous materials transportation. Given sequences $A$ $B$ Point Interests (POIs) visited road/transportation network, is to delete some nodes fr...

Aim There has been a wide interest in the effect of biotic interactions on species' occurrences and abundances at large spatial scales, coupled with vast development statistical methods to study them. Still, evidence for whether effects within-trophic-level (e.g. competition heterospecific attraction) are discernible beyond local scales remains inconsistent. Here, we present novel hypothesis-te...

Conventional pairwise constrained metric learning methods usually restrict the distance between samples of a similar pair to be lower than a fixed upper bound, and the distance between samples of a dissimilar pair higher than a fixed lower bound. Such fixed bound based constraints, however, may not work well when the intraand inter-class variations are complex. In this paper, we propose a shrin...