Efficient learning of an appropriate distance metric is an increasingly important problem in machine learning. However, current methods are limited by scalability issues or are unsuited to use with general similarity/dissimilarity constraints. In this paper, we propose an efficient metric learning method based on the max-margin framework with pairwise constraints that has a strong generalizatio...

one concern about using transgenic plants is the genetic variation that occurred from theirs tissue culture and regeneration. molecular markers are an important element for efficient and effective determination of genetic variation. the present work was carried out to assess the genetic uniformity of transgenic cottons (bt and chitinase lines), using rapd, issr molecular markers and sds-page an...

This study looks at the Japanese dance form, Butoh, and examines subjects’ similarity ratings of ten affects to see whether they form a circumplex structure. Naïve western viewers observed pairwise presentation of 15 second movie clips of dance segments devised to convey different affects. For each possible pairing of the ten affects observers provided a rating of dissimilarity of the dance mov...

In semisupervised fuzzy clustering, this article extends the traditional pairwise constraint (i.e., must-link or cannot-link) to constraint. The allows a supervisor provide grade of similarity dissimilarity between implicit vectors pair samples. This can represent more complicated relationship samples and avoid eliminating characteristics. Then, we propose clustering with constraints (SSFPC). n...

One concern about using transgenic plants is the genetic variation that occurred from theirs tissue culture and regeneration. Molecular markers are an important element for efficient and effective determination of genetic variation. The present work was carried out to assess the genetic uniformity of transgenic cottons (Bt and chitinase lines), using RAPD, ISSR molecular markers and SDS-PAGE an...

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

In recent years, metric learning in the semisupervised setting has aroused a lot of research interest. One type of semisupervised 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 well with the data set size. In this letter, we pro...