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factorization-methods
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Twin Edge Coloring of Graphs
, M.Sc. Thesis Sharif University of Technology ; Akbari, Saeed (Supervisor)
Abstract
Let G be a graph. A twin edge k-coloring of G is a proper edge coloring of G with the elements of Z_k so that for every vertex u and v of G we have s(u)≠s(v), where s(u) is the sum of all colors of the edges incident with u. The minimum k for which G has a twin edge k-coloring is called twin chromatic index of G and denoted by χ_t^' (G). In this thesis we find the chromatic index of paths, cycles, complete graphs, complete bipartite graphs and some complete tripartite graphs. In 2014 it was conjectured that if G is a connected graph with at least 3 vertices and maximum degree Δ(G), then χ_t^' (G)≤Δ(G)+3
Recurrent poisson factorization for temporal recommendation
, Article Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13 August 2017 through 17 August 2017 ; Volume Part F129685 , 2017 , Pages 847-855 ; 9781450348874 (ISBN) ; Alizadeh, K ; Khodadadi, A ; Arabzadeh, A ; Farajtabar, M ; Zha, H ; Rabiee, H. R ; Sharif University of Technology
Abstract
Poisson factorization is a probabilistic model of users and items for recommendation systems, where the so-called implicit consumer data is modeled by a factorized Poisson distribution. There are many variants of Poisson factorization methods who show state-of-the-art performance on real-world recommendation tasks. However, most of them do not explicitly take into account the temporal behavior and the recurrent activities of users which is essential to recommend the right item to the right user at the right time. In this paper, we introduce Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit...
Noise Analysis and Performance Improvement of Microwave Diode Mixers, and Improving the Method of Noise Figure Measurement
, Ph.D. Dissertation Sharif University of Technology ; Banai, Ali (Supervisor)
Abstract
In spite of developments in microwave mixers, reducing its noise figure is still a designers’ challenge in receiver applications. Due to generating intermodulations and saturating the following stages, the low noise amplifier cannot have a so much gain to decrease the noise of the following stages in a typical receiver. This problem is intensified in special applications because of low dynamic range of low noise amplifiers. Accordingly, the low noise amplifier is replaced sometimes by the mixer. Therefore, the design of a low noise mixer is an important issue in improving the noise performance of a total receiver.To this end, it is required to accurately analyse the nonlinear and noise...
Efficient iterative Semi-Supervised Classification on manifold
, Article Proceedings - IEEE International Conference on Data Mining, ICDM ; 2011 , Pages 228-235 ; 15504786 (ISSN); 9780769544090 (ISBN) ; Rabiee, H. R ; Shaban, A ; Soltani Farani, A ; National Science Foundation (NSF) - Where Discoveries Begin; University of Technology Sydney; Google; Alberta Ingenuity Centre for Machine Learning; IBM Research ; Sharif University of Technology
Abstract
Semi-Supervised Learning (SSL) has become a topic of recent research that effectively addresses the problem of limited labeled data. Many SSL methods have been developed based on the manifold assumption, among them, the Local and Global Consistency (LGC) is a popular method. The problem with most of these algorithms, and in particular with LGC, is the fact that their naive implementations do not scale well to the size of data. Time and memory limitations are the major problems faced in large-scale problems. In this paper, we provide theoretical bounds on gradient descent, and to overcome the aforementioned problems, a new approximate Newton's method is proposed. Moreover, convergence...