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Sparse Subspace Clustering: Algorithm, Theory, and Applications
TLDR
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.
Sparse Subspace Clustering: Algorithm, Theory, and Applications.
TLDR
An algorithm to cluster high-dimensional data points that lie in a union of low-dimensional subspaces is proposed and studied, which does not require initialization, can be solved efficiently, and can handle data points near the intersections of subspace.
Temporal Convolutional Networks for Action Segmentation and Detection
TLDR
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.
Generalized principal component analysis (GPCA)
  • R. Vidal, Yi Ma, S. Sastry
  • Computer Science, Mathematics
    IEEE Transactions on Pattern Analysis and Machine…
  • 1 December 2005
TLDR
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.
A Benchmark for the Comparison of 3-D Motion Segmentation Algorithms
TLDR
This paper compares four 3D motion segmentation algorithms for affine cameras on a benchmark of 155 motion sequences of checkerboard, traffic, and articulated scenes.
Berkeley MHAD: A comprehensive Multimodal Human Action Database
TLDR
A controlled multimodal dataset consisting of temporally synchronized and geometrically calibrated data from an optical motion capture system, multi-baseline stereo cameras from multiple views, depth sensors, accelerometers and microphones, provides researchers an inclusive testbed to develop and benchmark new algorithms across multiple modalities under known capture conditions in various research domains.
See all by looking at a few: Sparse modeling for finding representative objects
TLDR
The proposed framework and theoretical foundations are illustrated with examples in video summarization and image classification using representatives and can be extended to detect and reject outliers in datasets, and to efficiently deal with new observations and large datasets.
Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit
TLDR
This paper shows that the method based on orthogonal matching pursuit is both computationally efficient and guaranteed to give a subspace-preserving affinity under broad conditions and is the first one to handle 100,000 data points.
JHU-ISI Gesture and Skill Assessment Working Set ( JIGSAWS ) : A Surgical Activity Dataset for Human Motion Modeling
TLDR
A dataset of surgical activities captured using the da Vinci Surgical System and consisting of kinematic and video from eight surgeons with different levels of skill performing five repetitions of three elementary surgical tasks on a bench-top model is described.
Sparse Manifold Clustering and Embedding
TLDR
An algorithm called Sparse Manifold Clustering and Embedding (SMCE) for simultaneous clustering and dimensionality reduction of data lying in multiple nonlinear manifolds finds a small neighborhood around each data point and connects each point to its neighbors with appropriate weights.
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