Dimensionality reduction plays a vital role in pattern recognition. However, for normalized vector data, existing methods do not utilize the fact that the data is normalized. In this paper, we propose to employ an Angular Decomposition of the normalized vector data which corresponds to embedding them on a unit surface. On graph data for similarity/kernel matrices with constant diagonal elements...

The similarity between trajectory patterns in clustering has played an important role in discovering movement behaviour of different groups of mobile objects. Several approaches have been proposed to measure the similarity between sequences in trajectory data. Most of these measures are based on Euclidean space or on spatial network and some of them have been concerned with temporal aspect or o...

Biometrics as a method for identification systems has been widely used for its robustness; one of its used is based on human palm. Palm has unique characteristics to be used in personal identification because of it features. In this paper the palm features are extracted using Discrete Cosine Transform energy features, Wavelet Transform energy features and Sobel Code to obtain feature vectors an...

This paper evaluates the performance of different similarity measures in the context of document summarization. For this purpose in this paper a simple and effective sentence extractive technique is used. The proposed method is based on evaluation of relevance score of sentence. Many measures are available for the calculation of inter sentence relationships. To calculate a similarity between se...

In this paper, different types of web session similarity metrics are compared and combined for better web session clustering. Syntactic and co-occurrence information are used for similarity calculation. Syntactic information on a web page includes the place of the page in the directory hierarchy. Co-occurrence information is the amount of the occurrences of two web pages in the same sessions. V...

We present a matrix factorization model for learning cross-lingual representations for sentences. Using sentence-aligned corpora, the proposed model learns distributed representations by factoring the given data into language-dependent factors and one shared factor. As a result, input sentences from both languages can be mapped into fixed-length vectors and then compared directly using the cosi...

This paper explores methods to alleviate the effect of lexical sparseness in the classification of verbal arguments. We show how automatically generated selectional preferences are able to generalize and perform better than lexical features in a large dataset for semantic role classification. The best results are obtained with a novel second-order distributional similarity measure, and the posi...

This paper describes the system used by the LIPN team in the Semantic Textual Similarity task at SemEval 2013. It uses a support vector regression model, combining different text similarity measures that constitute the features. These measures include simple distances like Levenshtein edit distance, cosine, Named Entities overlap and more complex distances like Explicit Semantic Analysis, WordN...

Clustering is one of the very powerful and widely used technique in information retrieval. All clustering methods works on finding relationship among data objects. There are various similarity measures used along with criterion functions to find similarity between documents like cosine, jaccard etc. Clustering efficiency and performance is highly dependent on the accuracy of the similarity meas...

We present a novel approach to automatically extracting summary excerpts from audio and video. Our approach is to maximize the average similarity between the excerpt and the source. We first calculate a similarity matrix by comparing each pair of time samples using a quantitative similarity measure. To determine the segment with highest average similarity, we maximize the summation of the self-...