Trace Norm Regularized Tensor Classification and Its Online Learning Approaches

Details Trace Norm Regularized Tensor Classification and Its Online Learning Approaches Trace Norm Regularized Tensor Classification and Its Online Learning Approaches

2011-09-07

arxiv.org/abs/1109.1342v1

Computer Science

Numerical Analysis

11 pages, 2 figures

Scientific paper

In this paper we propose an algorithm to classify tensor data. Our methodology is built on recent studies about matrix classification with the trace norm constrained weight matrix and the tensor trace norm. Similar to matrix classification, the tensor classification is formulated as a convex optimization problem which can be solved by using the off-the-shelf accelerated proximal gradient (APG) method. However, there are no analytic solutions as the matrix case for the updating of the weight tensors via the proximal gradient. To tackle this problem, the Douglas-Rachford splitting technique and the alternating direction method of multipliers (ADM) used in tensor completion are adapted to update the weight tensors. Further more, due to the demand of real applications, we also propose its online learning approaches. Experiments demonstrate the efficiency of the methods.

Affiliated with

Also associated with

No associations

Rating

Trace Norm Regularized Tensor Classification and Its Online Learning Approaches does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Trace Norm Regularized Tensor Classification and Its Online Learning Approaches, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Trace Norm Regularized Tensor Classification and Its Online Learning Approaches will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-305461