| Paper: | MLSP-P6.4 | ||
| Session: | Learning Theory and Models | ||
| Time: | Thursday, May 20, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Learning Theory and Modeling | ||
| Title: | A NEW WAY OF PCA: INTEGRATED-SQUARED-ERROR AND EM ALGORITHMS | ||
| Authors: | Jong-Hoon Ahn; POSTECH | ||
| Seungjin Choi; POSTECH | |||
| Jong-Hoon Oh; POSTECH | |||
| Abstract: | Minimization of reconstruction error (squared-error) leads to principal subspace analysis (PSA) which estimates scaled and rotated principal axes of a set of observed data. In this paper we introduce a new alternative error, so called, integratedsquared-error, the minimization of which determines the exact principal axes (without rotational ambiguity) of a set of observed data. We consider the properties of the integrated-squared-error in the framework of coupled generative model, giving efficient EM algorithms for integrated-squared-error minimization. We revisit the generalized Hebbian algorithm (GHA) and show that it emerges from integrated-squared-error minimization in a single-layer linear feedforward neural network. | ||
| Back | |||
Home -||-
Organizing Committee -||-
Technical Committee -||-
Technical Program -||-
Plenaries
Paper Submission -||-
Special Sessions -||-
ITT -||-
Paper Review -||-
Exhibits -||-
Tutorials
Information -||-
Registration -||-
Travel Insurance -||-
Housing -||-
Workshops