Researchers have typically concentrated on analyzing what happens internally in a complex network and using this to distinguish between nodes. However, there has been less effort towards comparing between different networks. In this paper, we proposed a novel approach to rank alternative complex networks based on their performances. We consider this as a ranking problem in decision analysis bas...

This paper describes a numerical method for examining similarities among tempo and loudness features extracted from recordings of the same musical work and evaluates its effectiveness compared to Pearson correlation. Starting with correlation at multiple timescales, other concepts such as a performance “noise-floor” are used to generate measurements which are more refined than correlation alone...

The visual estimation of the rank of a matrix has eluded researchers across a myriad of disciplines many years. In this paper, we demonstrate the successful visual estimation of a matrix’s rank by treating it as a classification problem. When tested on a dataset of tens-of-thousands of colormapped matrices of varying ranks, we not only achieve state-of-the-art performance, but also distressingl...

Determinantal point processes (DPPs) are an elegant model for encoding probabilities over subsets, such as shopping baskets, of a ground set, such as an item catalog. They are useful for a number of machine learning tasks, including product recommendation. DPPs are parametrized by a positive semi-definite kernel matrix. Recent work has shown that using a low-rank factorization of this kernel pr...

Data envelopment analysis provides a theoretical framework for analyzing performance and measuring efficiency. Using data envelopment analysis, we can evaluate units and organizations and discuss about their efficiency or inefficiency. In performance assessment of organizations, the most important goal is to rank the units according to their efficiency. Since ranking decision making units in da...

Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in machine learning, control theory, and discrete geometry. This class of optimization problems, known as rank minimization, is NP-HARD, and for most practical problems there are no efficient algorithms that yield exact solutions. A popular heuristic replaces the rank function with t...

Learning to rank is the problem of ranking objects by using machine learning techniques. One of the applications of learning to rank is for ranking document of search results. In this research, we compare the performance of three learning to rank algorithms: RankSVM, LambdaMART, and Additive Groves. RankSVM, which is ranking variant of the classical SVM algorithm, is commonly used as a baseline...