Praneeth Narayanamurthy

560 citations
24 papers · 400 indexed · 1 hit paper · h-index 8

Praneeth Narayanamurthy

23 papers receiving 394 citations

Hit Papers

Robust Subspace Learning: Robust PCA, Robust Subspace Tra...262201820262020202350100150200250

Peers

Praneeth Narayanamurthy
Comparison fields: 5 of 62
  • Computational Mathematics 19
  • Computer Vision and Pattern Recognition 194
  • Signal Processing 90
  • Computational Mechanics 170
  • Media Technology 61
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Countries citing papers authored by Praneeth Narayanamurthy

Since Specialization
Citations

This map shows the geographic impact of Praneeth Narayanamurthy's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Praneeth Narayanamurthy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Praneeth Narayanamurthy more than expected).

Fields of papers citing papers by Praneeth Narayanamurthy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Praneeth Narayanamurthy. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Praneeth Narayanamurthy. The network helps show where Praneeth Narayanamurthy may publish in the future.

Co-authorship network

The 7 scholars most cited alongside Praneeth Narayanamurthy, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Praneeth Narayanamurthy Line = papers co-authored together Praneeth Narayanamurthy links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20241
2 20240
3 20233
4 20229
5
Federated Over-the-Air Subspace Learning from Incomplete Data.
20201
6 20208
7 202012
8
Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements
20197
9 20193
10 201917
11
Subspace Tracking from Missing and Outlier Corrupted Data.
20182
12
Robust PCA and Robust Subspace Tracking: A Comparative Evaluation
20181
13 201813
14 20187
15 201825
16
Robust PCA and Robust Subspace Tracking.
20174
17
New Results for Provable Dynamic Robust PCA.
20171
18
Nearly Optimal Robust Subspace Tracking and Dynamic Robust PCA
20172
19
MEDRoP: Memory-Efficient Dynamic Robust PCA.
20176
20 201711

About Praneeth Narayanamurthy

Praneeth Narayanamurthy is a scholar working on Acoustics and Ultrasonics, Computational Mechanics, Signal Processing, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 24 papers that have together received 400 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (19 papers), Distributed Sensor Networks and Detection Algorithms (9 papers), Blind Source Separation Techniques (7 papers), Microwave Imaging and Scattering Analysis (5 papers), Image and Signal Denoising Methods (4 papers), Indoor and Outdoor Localization Technologies (4 papers), Target Tracking and Data Fusion in Sensor Networks (3 papers) and Structural Health Monitoring Techniques (2 papers). The work is most often cited by research in Computational Mathematics (19 citations), Computer Vision and Pattern Recognition (194 citations), Signal Processing (90 citations), Computational Mechanics (170 citations) and Media Technology (61 citations). Praneeth Narayanamurthy has collaborated with scholars based in United States, South Korea and United Kingdom. Frequent co-authors include Namrata Vaswani, Thierry Bouwmans, Sajid Javed, Aditya Ramamoorthy, Shaama Mallikarjun Sharada, Selin Bac and Urbashi Mitra. Their work appears in journals such as IEEE Transactions on Signal Processing, IEEE Signal Processing Magazine, IEEE Transactions on Information Theory, arXiv (Cornell University) and IEEE Journal on Selected Areas in Information Theory.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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