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» Reduced Rank Kernel Ridge Regression
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NPL
2002
168views more  NPL 2002»
13 years 9 months ago
Reduced Rank Kernel Ridge Regression
Ridge regression is a classical statistical technique that attempts to address the bias-variance trade-off in the design of linear regression models. A reformulation of ridge regr...
Gavin C. Cawley, Nicola L. C. Talbot
CSDA
2007
128views more  CSDA 2007»
13 years 9 months ago
Regularized linear and kernel redundancy analysis
Redundancy analysis (RA) is a versatile technique used to predict multivariate criterion variables from multivariate predictor variables. The reduced-rank feature of RA captures r...
Yoshio Takane, Heungsun Hwang
ECWEB
2009
Springer
204views ECommerce» more  ECWEB 2009»
14 years 3 months ago
Computational Complexity Reduction for Factorization-Based Collaborative Filtering Algorithms
Abstract. Alternating least squares (ALS) is a powerful matrix factorization (MF) algorithm for both implicit and explicit feedback based recommender systems. We show that by using...
István Pilászy, Domonkos Tikk
ECCV
2010
Springer
13 years 11 months ago
Bilinear Kernel Reduced Rank Regression for Facial Expression Synthesis
In the last few years, Facial Expression Synthesis (FES) has been a flourishing area of research driven by applications in character animation, computer games, and human computer ...
AAAI
2011
12 years 9 months ago
Trajectory Regression on Road Networks
This paper addresses the task of trajectory cost prediction, a new learning task for trajectories. The goal of this task is to predict the cost for an arbitrary (possibly unknown)...
Tsuyoshi Idé, Masashi Sugiyama