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» Learning the Relative Importance of Features in Image Data
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WSDM
2012
ACM
352views Data Mining» more  WSDM 2012»
12 years 4 months ago
Multi-relational matrix factorization using bayesian personalized ranking for social network data
A key element of the social networks on the internet such as Facebook and Flickr is that they encourage users to create connections between themselves, other users and objects. On...
Artus Krohn-Grimberghe, Lucas Drumond, Christoph F...
NIPS
2000
13 years 10 months ago
Rate-coded Restricted Boltzmann Machines for Face Recognition
We describe a neurally-inspired, unsupervised learning algorithm that builds a non-linear generative model for pairs of face images from the same individual. Individuals are then ...
Yee Whye Teh, Geoffrey E. Hinton
ICONIP
2007
13 years 10 months ago
Using Image Stimuli to Drive fMRI Analysis
We introduce a new unsupervised fMRI analysis method based on Kernel Canonical Correlation Analysis which differs from the class of supervised learning methods that are increasing...
David R. Hardoon, Janaina Mourão Miranda, M...
ICIP
2004
IEEE
14 years 10 months ago
Steganalysis of quantization index modulation data hiding
Quantization index modulation (QIM) techniques have been gaining popularity in the data hiding community because of their robustness and information-theoretic optimality against a...
Kenneth Sullivan, Zhiqiang Bi, Upamanyu Madhow, Sh...
CVPR
2009
IEEE
15 years 2 months ago
Learning Mixed Templates for Object Recognition
This article proposes a method for learning object templates composed of local sketches and local textures, and investigates the relative importance of the sketches and textures ...
Haifeng Gong, Song Chun Zhu, Ying Nian Wu, Zhangzh...