Recent work in distance metric learning focused on learning transformations of data that best align with provided sets of pairwise similarity and dissimilarity constraints. The learned transformations lead to improved retrieval, classification, and clustering algorithms due to the more accurate distance or similarity measures. Here, we introduce the problem of learning these transformations whe...

To improve unconstrained iris recognition system performance in different environments, a performance improvement method of unconstrained iris recognition based on domain adaptation metric learning is proposed. A kernel matrix is calculated as the solution of domain adaptation metric learning. The known Hamming distance computing by intra-class and inter-class is used as the optimization learni...

In the real world, it is common that different experts have different opinions on the same problem due to their different experience. How to come up with a consistent decision becomes a critical issue. As an example, patient similarity assessment is an important task in the context of patient cohort identification for comparative effectiveness studies and clinical decision support applications....

The problem of unsupervised and semi-supervised clustering is extensively studied in machine learning. In order to involve user in image data clustering, (Lai et al., 2014) proposed a new approache for interactive semi-supervised clustering that translates the feedback of user (expressed at the level of individual images) into pairwise constraints between groups of images, these groupes being c...

Metric learning algorithms produce distance metrics that capture the important relationships among data. In this work, we study the connection between metric learning and collaborative filtering. We propose Collaborative Metric Learning (CML) which learns a joint metric space to encode not only users’ preferences but also the user-user and itemitem similarity. The proposed algorithm outperforms...

Unsupervised phoneme segmentation aims at dividing a speech stream into phonemes without using any prior knowledge of linguistic contents and acoustic models. In [1], we formulated this problem into an optimization framework, and developed an objective function, summation of squared error (SSE) based on the Euclidean distance of cepstral features. However, it is unknown whether or not Euclidean...

Research in interactive machine learning has shown the effectiveness of live, human interaction with machine learning algorithms in many applications. Metric learning is a common type of algorithm employed in this context, using feedback from users to learn a distance metric over the data that encapsulates their own understanding. Less progress has been made on helping users decide which data t...

Multi-metric learning techniques learn local metric tensors in different parts of a feature space. With such an approach, even simple classifiers can be competitive with the state-of-the-art because the distance measure locally adapts to the structure of the data. The learned distance measure is, however, non-metric, which has prevented multi-metric learning from generalizing to tasks such as d...

In this paper, we prove some properties of algebraic cone metric spaces and introduce the notion of algebraic distance in an algebraic cone metric space. As an application, we obtain some famous fixed point results in the framework of this algebraic distance.

In this study a new approach to rank exponential fuzzy numbers using  -cuts is established. The metric distance of the interval numbers is extended to exponential fuzzy numbers. By using the ranking of exponential fuzzy numbers and using  -cuts the critical path of a project network is solved and illustrated by numerical examples. Keywords: Exponential Fuzzy Numbers,  -cuts, Metric Dista...