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» Reduced Rank Kernel Ridge Regression
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NPL
2002
168views more  NPL 2002»
9 years 2 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»
9 years 2 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»
9 years 9 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
9 years 4 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
8 years 2 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
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