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» Learning from Highly Structured Data by Decomposition
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SDM
2009
SIAM
202views Data Mining» more  SDM 2009»
15 years 6 months ago
Proximity-Based Anomaly Detection Using Sparse Structure Learning.
We consider the task of performing anomaly detection in highly noisy multivariate data. In many applications involving real-valued time-series data, such as physical sensor data a...
Tsuyoshi Idé, Aurelie C. Lozano, Naoki Abe,...
CVPR
2010
IEEE
15 years 5 months ago
Hierarchical Convolutional Sparse Image Decomposition
Building robust low and mid-level image representations, beyond edge primitives, is a long-standing goal in vision. Many existing feature detectors spatially pool edge information...
Matthew Zeiler, Dilip Krishnan, Graham Taylor, Rob...
ICCV
2011
IEEE
13 years 9 months ago
Automated Articulated Structure and 3D Shape Recovery from Point Correspondences
In this paper we propose a new method for the simultaneous segmentation and 3D reconstruction of interest point based articulated motion. We decompose a set of point tracks into r...
Joao Fayad, Chris Russell, Lourdes Agapito
66
Voted
CVPR
2008
IEEE
15 years 11 months ago
Decomposition, discovery and detection of visual categories using topic models
We present a novel method for the discovery and detection of visual object categories based on decompositions using topic models. The approach is capable of learning a compact and...
Mario Fritz, Bernt Schiele
COLT
2010
Springer
14 years 7 months ago
Principal Component Analysis with Contaminated Data: The High Dimensional Case
We consider the dimensionality-reduction problem (finding a subspace approximation of observed data) for contaminated data in the high dimensional regime, where the number of obse...
Huan Xu, Constantine Caramanis, Shie Mannor