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TIFS
2010
82views more  TIFS 2010»
15 years 23 days ago
Lossless data embedding in electronic inks
—This paper presents a novel lossless data embedding algorithm for electronic inks. The proposed algorithm first computes the analytical ink-curve for each stroke as a set of sm...
Hong Cao, Alex C. Kot
149
Voted
TIP
2010
154views more  TIP 2010»
15 years 23 days ago
Projective Nonnegative Graph Embedding
—We present in this paper a general formulation for nonnegative data factorization, called projective nonnegative graph embedding (PNGE), which 1) explicitly decomposes the data ...
Xiaobai Liu, Shuicheng Yan, Hai Jin
122
Voted
CVPR
2005
IEEE
16 years 4 months ago
Rank-R Approximation of Tensors: Using Image-as-Matrix Representation
We present a novel multilinear algebra based approach for reduced dimensionality representation of image ensembles. We treat an image as a matrix, instead of a vector as in tradit...
Hongcheng Wang, Narendra Ahuja
KDD
2004
ACM
166views Data Mining» more  KDD 2004»
16 years 2 months ago
Predicting prostate cancer recurrence via maximizing the concordance index
In order to effectively use machine learning algorithms, e.g., neural networks, for the analysis of survival data, the correct treatment of censored data is crucial. The concordan...
Lian Yan, David Verbel, Olivier Saidi
STOC
2004
ACM
145views Algorithms» more  STOC 2004»
16 years 2 months ago
Using mixture models for collaborative filtering
A collaborative filtering system at an e-commerce site or similar service uses data about aggregate user behavior to make recommendations tailored to specific user interests. We d...
Jon M. Kleinberg, Mark Sandler