R
René Vidal
Researcher at Johns Hopkins University
Publications - 395
Citations - 29712
René Vidal is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Cluster analysis & Linear subspace. The author has an hindex of 78, co-authored 378 publications receiving 25698 citations. Previous affiliations of René Vidal include Princeton University & IMEC.
Papers
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Journal ArticleDOI
Sparse Subspace Clustering: Algorithm, Theory, and Applications
Ehsan Elhamifar,René Vidal +1 more
TL;DR: In this article, a sparse subspace clustering algorithm is proposed to cluster high-dimensional data points that lie in a union of low-dimensional subspaces, where a sparse representation corresponds to selecting a few points from the same subspace.
Posted Content
Sparse Subspace Clustering: Algorithm, Theory, and Applications
Ehsan Elhamifar,René Vidal +1 more
TL;DR: This paper proposes and studies an algorithm, called sparse subspace clustering, to cluster data points that lie in a union of low-dimensional subspaces, and demonstrates the effectiveness of the proposed algorithm through experiments on synthetic data as well as the two real-world problems of motion segmentation and face clustering.
Proceedings ArticleDOI
Sparse subspace clustering
Ehsan Elhamifar,René Vidal +1 more
TL;DR: This work proposes a method based on sparse representation (SR) to cluster data drawn from multiple low-dimensional linear or affine subspaces embedded in a high-dimensional space and applies this method to the problem of segmenting multiple motions in video.
Journal ArticleDOI
Generalized principal component analysis (GPCA)
TL;DR: An algebro-geometric solution to the problem of segmenting an unknown number of subspaces of unknown and varying dimensions from sample data points and applications of GPCA to computer vision problems such as face clustering, temporal video segmentation, and 3D motion segmentation from point correspondences in multiple affine views are presented.
Proceedings ArticleDOI
Temporal Convolutional Networks for Action Segmentation and Detection
TL;DR: A class of temporal models that use a hierarchy of temporal convolutions to perform fine-grained action segmentation or detection, which are capable of capturing action compositions, segment durations, and long-range dependencies, and are over a magnitude faster to train than competing LSTM-based Recurrent Neural Networks.