An instance can be easily depicted from different views in pattern recognition, and it is desirable to exploit the information of these complement each other. However, most metric learning or similarity methods are developed for single-view feature representation over past two decades, which not suitable dealing with multi-view data directly. In this paper, we propose a cosine (MVCSL) approach ...

The paper presents the correlation and correlation coefficient of single-valued neutrosophic sets (SVNSs) based on the extension of the correlation of intuitionistic fuzzy sets and demonstrates that the cosine similarity measure is a special case of the correlation coefficient in SVNS. Then a decision-making method is proposed by the use of the weighted correlation coefficient or the weighted c...

The i-vector model is widely used by the state-of-the-art speaker recognition system. We proposed a new Mahalanobis metric scoring learned from weighted pairwise constraints (WPCML), which use the different weights for the empirical error of the similar and dissimilar pairs. In the new i-vector space described by the metric, the distance between the same speaker’s i-vectors is small, while that...

In this paper, we present a comparison of collocation-based similarity measures: Jaccard, Dice and Cosine similarity measures for the proper selection of additional search terms in query expansion. In addition, we consider two more similarity measures: average conditional probability (ACP) and normalized mutual information (NMI). ACP is the mean value of two conditional probabilities between a ...

This paper describes an approach to implementing a tool for evaluating semantic similarity. We investigated the potential benefits of (1) using text summarisation to narrow down the comparison to the most important concepts in both texts, and (2) leveraging WordNet information to increase usefulness of cosine comparisons of short texts. In our experiments, text summarisation using a graph-based...

Text classification is a process where documents are categorized usually by topic, place, readability easiness, etc. For text classification by topic, a well-known method is Singular Value Decomposition. For text classification by readability, “Flesh Reading Ease index” calculates the readability easiness level of a document (e.g. easy, medium, advanced). In this paper, we propose Singular Valu...

One of the problems addressed by the Tracker project is the extraction of the key issues discussed at meetings through the analysis of transcripts. Whilst the task of topic extraction is an easy task for humans it has proven difficult task to automate given the unstructured nature of our transcripts. This paper proposes a new approach to transcript segmentation based on the Utterance Cosine Sim...

Proximity rank join is the problem of finding the top-K combinations with the highest aggregate score in which the best combinations of objects coming from different services are sought, and each object is equipped with both a score and a real-valued feature vector. The proximity of the objects i.e. the geometry of the feature space plays a distinctive role in the computation of the overall sco...