no code implementations • 11 May 2023 • Yingqiang Ge, Mostafa Rahmani, Athirai Irissappane, Jose Sepulveda, James Caverlee, Fei Wang
In real-world scenarios, most platforms collect both large-scale, naturally noisy implicit feedback and small-scale yet highly relevant explicit feedback.
no code implementations • 31 May 2022 • 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.
no code implementations • 8 Jan 2022 • Mostafa Rahmani
A novel theoretical study is presented which sheds light on the key performance factors of both algorithms (MFC/iPursuit) and it is shown that both algorithms can be robust to notable intersections between the span of clusters.
no code implementations • 16 Aug 2021 • Mostafa Rahmani, Rasoul Shafipour, Ping Li
The proposed approach is used to design several novel global feature aggregation methods based on the choice of the LFDS.
no code implementations • 16 Aug 2021 • Weiwei Li, Mostafa Rahmani, Ping Li
It is shown that in contrast to most of the existing methods which require the subspaces to be sufficiently incoherent with each other, Innovation Pursuit only requires the innovative components of the subspaces to be sufficiently incoherent with each other.
no code implementations • 23 Jun 2021 • Mostafa Rahmani, Ping Li
In the application of Innovation Search for outlier detection, the directions of innovation were utilized to measure the innovation of the data points.
no code implementations • 30 Dec 2019 • Mostafa Rahmani, Ping Li
In this paper, we present a new discovery that the directions of innovation can be used to design a provable and strong robust (to outlier) PCA method.
no code implementations • NeurIPS 2019 • Mostafa Rahmani, Ping Li
In other word, an outlier carries some innovation with respect to most of the other data points.
no code implementations • 3 Oct 2019 • Mostafa Rahmani, Ping Li
The proposed approach leverages a spatial representation of the graph which makes the neural network aware of the differences between the nodes and also their locations in the graph.
no code implementations • ICLR 2019 • Mostafa Rahmani, Ping Li
In the second step, the GNN is applied to the point-cloud representation of the graph provided by the embedding method.
no code implementations • 25 May 2018 • Mostafa Rahmani, Andre Beckus, Adel Karimian, George Atia
Uniform random node sampling is shown to improve the computational complexity over clustering of the full graph when the cluster sizes are balanced.
no code implementations • 4 Dec 2017 • Mostafa Rahmani, George Atia
An important problem in training deep networks with high capacity is to ensure that the trained network works well when presented with new inputs outside the training dataset.
no code implementations • ICML 2017 • Mostafa Rahmani, George Atia
To the best of our knowledge, this is the first provable robust PCA algorithm that is simultaneously non-iterative, can tolerate a large number of outliers and is robust to linearly dependent outliers.
no code implementations • ICML 2017 • Mostafa Rahmani, George Atia
Remarkably, the proposed approach can provably yield exact clustering even when the subspaces have significant intersections.
no code implementations • 12 Jun 2017 • Mostafa Rahmani, George Atia
This letter presents a new spectral-clustering-based approach to the subspace clustering problem.
no code implementations • 9 May 2017 • Mostafa Rahmani, George Atia
Random column sampling is not guaranteed to yield data sketches that preserve the underlying structures of the data and may not sample sufficiently from less-populated data clusters.
no code implementations • 7 Feb 2017 • Mostafa Rahmani, George Atia
Our approach hinges on the sparse approximation of a sparsely corrupted column so that the sparse expansion of a column with respect to the other data points is used to distinguish a sparsely corrupted inlier column from an outlying data point.
no code implementations • 18 Nov 2016 • Mostafa Rahmani, George Atia
Conventional sampling techniques fall short of drawing descriptive sketches of the data when the data is grossly corrupted as such corruptions break the low rank structure required for them to perform satisfactorily.
no code implementations • 15 Sep 2016 • Mostafa Rahmani, George Atia
As inliers lie in a low dimensional subspace and are mostly correlated, an inlier is likely to have strong mutual coherence with a large number of data points.
no code implementations • 2 Dec 2015 • Mostafa Rahmani, George Atia
This paper presents a new approach dubbed Innovation Pursuit (iPursuit) to the problem of subspace clustering using a new geometrical idea whereby subspaces are identified based on their relative novelties.
no code implementations • 21 May 2015 • Mostafa Rahmani, George Atia
This paper explores and analyzes two randomized designs for robust Principal Component Analysis (PCA) employing low-dimensional data sketching.
no code implementations • 1 Feb 2015 • Mostafa Rahmani, George Atia
In this paper, a scalable subspace-pursuit approach that transforms the decomposition problem to a subspace learning problem is proposed.