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M. Rahmani

9 papers hereh-index 14733 citations61 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author1
  • first author7
  • middle author1

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • stat.ML4
  • cs.LG3
  • cs.CV2
same name
  • M. Rahmani — 11 papers
  • M. Rahmani — 9 papers, h 43
  • M. Rahmani — 7 papers
  • M. Rahmani — 6 papers, h 9
  • M. Rahmani — 5 papers
  • M. Rahmani — 4 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172022
most citedSubspace Clustering via Optimal Direction Search

11 citations · 24 across the 8 of their papers we have counts for

collaborators
Showing stat.MLShow all

4 papers · 1 filter

stat.ML2022

Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series

Mostafa Rahmani, Anoop Deoras, Laurent Callot

This paper presents a novel, closed-form, and data/computation efficient online anomaly detection algorithm for time-series data. The proposed method, dubbed RPE, is a window-based…

stat.ML2021

Non-Local Feature Aggregation on Graphs via Latent Fixed Data Structures

Mostafa Rahmani, Rasoul Shafipour, Ping Li

In contrast to image/text data whose order can be used to perform non-local feature aggregation in a straightforward way using the pooling layers, graphs lack the tensor representa…

stat.ML2021★ 10 cited

Closed-Form, Provable, and Robust PCA via Leverage Statistics and Innovation Search

Mostafa Rahmani, Ping Li

The idea of Innovation Search, which was initially proposed for data clustering, was recently used for outlier detection. In the application of Innovation Search for outlier detect…

stat.ML2019★ 2 cited

Outlier Detection and Data Clustering via Innovation Search

Mostafa Rahmani, Ping Li

The idea of Innovation Search was proposed as a data clustering method in which the directions of innovation were utilized to compute the adjacency matrix and it was shown that Inn…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.