2.2k citations · 5.4k across the 90 of their papers we have counts for
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Learning Transformations for Classification Forests
Qiang Qiu, Guillermo Sapiro
This work introduces a transformation-based learner model for classification forests. The weak learner at each split node plays a crucial role in a classification tree. We propose…
Sparse similarity-preserving hashing
Jonathan Masci, Alex M. Bronstein, Michael M. Bronstein +2
In recent years, a lot of attention has been devoted to efficient nearest neighbor search by means of similarity-preserving hashing. One of the plights of existing hashing techniqu…
Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching
Marcelo Fiori, Pablo Sprechmann, Joshua Vogelstein +2
Graph matching is a challenging problem with very important applications in a wide range of fields, from image and video analysis to biological and biomedical problems. We propose…
Learning Transformations for Clustering and Classification
Qiang Qiu, Guillermo Sapiro
A low-rank transformation learning framework for subspace clustering and classification is here proposed. Many high-dimensional data, such as face images and motion sequences, appr…
Domain-invariant Face Recognition using Learned Low-rank Transformation
Qiang Qiu, Guillermo Sapiro, Ching-Hui Chen
We present a low-rank transformation approach to compensate for face variations due to changes in visual domains, such as pose and illumination. The key idea is to learn discrimina…
Learning Robust Subspace Clustering
Qiang Qiu, Guillermo Sapiro
We propose a low-rank transformation-learning framework to robustify subspace clustering. Many high-dimensional data, such as face images and motion sequences, lie in a union of lo…