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» Learning from Highly Structured Data by Decomposition
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ML
2010
ACM
15 years 2 months ago
Semi-supervised local Fisher discriminant analysis for dimensionality reduction
When only a small number of labeled samples are available, supervised dimensionality reduction methods tend to perform poorly due to overfitting. In such cases, unlabeled samples ...
Masashi Sugiyama, Tsuyoshi Idé, Shinichi Na...
CORR
2010
Springer
177views Education» more  CORR 2010»
15 years 3 months ago
Supervised Random Walks: Predicting and Recommending Links in Social Networks
Predicting the occurrence of links is a fundamental problem in networks. In the link prediction problem we are given a snapshot of a network and would like to infer which interact...
Lars Backstrom, Jure Leskovec
RIVF
2008
15 years 5 months ago
Simple but effective methods for combining kernels in computational biology
Complex biological data generated from various experiments are stored in diverse data types in multiple datasets. By appropriately representing each biological dataset as a kernel ...
Hiroaki Tanabe, Tu Bao Ho, Canh Hao Nguyen, Saori ...
ECCV
2002
Springer
16 years 5 months ago
Audio-Video Sensor Fusion with Probabilistic Graphical Models
Abstract. We present a new approach to modeling and processing multimedia data. This approach is based on graphical models that combine audio and video variables. We demonstrate it...
Matthew J. Beal, Hagai Attias, Nebojsa Jojic
KDD
2012
ACM
178views Data Mining» more  KDD 2012»
13 years 6 months ago
Differentially private transit data publication: a case study on the montreal transportation system
With the wide deployment of smart card automated fare collection (SCAFC) systems, public transit agencies have been benefiting from huge volume of transit data, a kind of sequent...
Rui Chen, Benjamin C. M. Fung, Bipin C. Desai, N&e...