[Research] Lab meeting: Wednesday June *9*th

[Karen Widmaier]

I've reserved NSH 1507 for Wednesday, June 9th from noon until 1:30pm.
Karen

-----Original Message-----
From: research-bounces at autonlab.org [mailto:research-bounces at autonlab.org]
On Behalf Of Artur Dubrawski
Sent: Thursday, June 03, 2010 6:25 PM
To: research at autonlab.org
Subject: Re: [Research] Lab meeting: Wednesday June *9*th

Well, of course Wednesday is June 9th.
Darned European calendars!

A.

Artur Dubrawski wrote:
> Speaker: Yi Zhang
> Title and Abstract: See below.
> Time: Noon, as usual
> Place: Karen W. will provide info
> Pizza: Yes
>
> See you all there!
> Artur
>
> ---
> Projection Penalties: Dimension Reduction without Loss
> Yi Zhang and Jeff Schneider
>
> Dimension reduction is popular for learning predictive models in 
> high-dimensional spaces. It can highlight the relevant part of the 
> feature space and avoid the curse of dimensionality. However, it can 
> also be harmful because any reduction loses information. In this paper, 
> we propose the \textit{projection penalty} framework to make use of 
> dimension reduction without losing valuable information.
>
> Reducing the feature space before learning predictive models can be 
> viewed as restricting the model search to some parameter subspace. The 
> idea of projection penalties is that instead of restricting the search 
> to a parameter subspace, we can search in the full space but penalize 
> the projection distance to this subspace. Dimension reduction is used to 
> guide the search, rather than to restrict it.
>
> We propose projection penalties for linear dimension reduction, and then 
> generalize to kernel-based reduction and other nonlinear methods. We 
> test projection penalties with various dimension reduction techniques in 
> different prediction tasks, including principal component regression and 
> partial least squares in regression tasks, kernel dimension reduction in 
> face recognition, and latent topic modeling in text classification. 
> Experimental results show that projection penalties are a more effective 
> and reliable way to make use of dimension reduction techniques.
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