Himanshu Nayar

Himanshu Nayar

Data Scientist, ML researcher

Posts by Collection

portfolio

publications

Multicarrier modulation with variable peak-to-average power ratio using partial fast Fourier transform

Published in IET Communications 9.12 (2015), 2012

The authors present a novel frequency division multiplexing scheme which can generate a signal whose worst-case peak-to-average power ratio (PAPR) is tunable by an input parameter; they call this scheme tunable PAPR frequency division multiplexing (TP-FDM). Special cases of TP-FDM are orthogonal FDM (OFDM) and single carrier (SC) modulation.

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Theoretical Performance Analysis of Tucker Higher Order SVD in Extracting Structure from Multiple Signal-plus-Noise Matrices

Published in 2014 48th Asilomar Conference on Signals, Systems and Computers. IEEE, 2014

The Tucker Higher Order SVD is a popular algorithm for uncovering structure from tensor datacubes. This algorithm has been successfully used in many signal processing, machine learning and data mining applications. In this work, we use recent results from random matrix theory to analyze the performance of the HOSVD algorithm. In particular, we focus on the performance HOSVD on 3-D tensors for extraction of structure from signal-plus-noise tensors. We analyze the missing data setting where the entries of the signal-plus-noise tensor are randomly deleted

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OptFuse: Low-rank Factor Estimation by Optimal Data-Driven Linear Fusion of Multiple Signal-Plus-Noise Matrices

Published in 18th International Conference on Information Fusion, 2015

We consider the setting where we are given multiple signal-plus-noise matrices. The signal matrices are modeled as low-rank with the same factors (or eigenvectors) but arbitrary (modulo a fixed ordering) eigen-SNRs. One motivating example is the determination of community structure from multiple, independent adjacency matrices. The objective is to combine them linearly so that the eigenvectors of the resulting matrix are as close as possible to the unknown, latent factors. We utilize recent results from random matrix theory to recast this as a constrained data-driven optimization problem and develop an efficient algorithm (OptFuse) for solving it. We demonstrate the improved performance of the algorithm relative to an equal weighting scheme.

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Improved Hidden Clique Detection by Optimal Linear Fusion of Multiple Adjacency Matrices

Published in 49th Asilomar Conference on Signals, Systems and Computers. IEEE, 2015

Graph fusion has emerged as a promising research area for addressing challenges associated with noisy, uncertain, multi-source data. While many ad-hoc graph fusion techniques exist in the current literature, an analytical approach for analyzing the fundamentals of the graph fusion problem is lacking. We consider the setting where we are given multiple Erdos-R ˝ enyi ´ modeled adjacency matrices containing a common hidden or planted clique. The objective is to combine them linearly so that the principal eigenvectors of the resulting matrix best reveal the vertices associated with the clique. We utilize recent results from random matrix theory to derive the optimal weighting coefficients and use these insights to develop a data-driven fusion algorithm

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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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