Tensorized LSSVMs for Multitask Regression

• URL: http://arxiv.org/abs/2303.02451v1
• Date: Sat, 4 Mar 2023 16:36:03 GMT
• Title: Tensorized LSSVMs for Multitask Regression
• Authors: Jiani Liu, Qinghua Tao, Ce Zhu, Yipeng Liu, Johan A.K. Suykens
• Abstract summary: Multitask learning (MTL) can utilize the relatedness between multiple tasks for performance improvement. New MTL is proposed by leveraging low-rank tensor analysis and Least Squares Support Vectorized Least Squares Support Vectorized tLSSVM-MTL.
• Score: 48.844191210894245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multitask learning (MTL) can utilize the relatedness between multiple tasks for performance improvement. The advent of multimodal data allows tasks to be referenced by multiple indices. High-order tensors are capable of providing efficient representations for such tasks, while preserving structural task-relations. In this paper, a new MTL method is proposed by leveraging low-rank tensor analysis and constructing tensorized Least Squares Support Vector Machines, namely the tLSSVM-MTL, where multilinear modelling and its nonlinear extensions can be flexibly exerted. We employ a high-order tensor for all the weights with each mode relating to an index and factorize it with CP decomposition, assigning a shared factor for all tasks and retaining task-specific latent factors along each index. Then an alternating algorithm is derived for the nonconvex optimization, where each resulting subproblem is solved by a linear system. Experimental results demonstrate promising performances of our tLSSVM-MTL.

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