IEEE Trans Pattern Anal Mach Intell - Active Learning Based on Locally Linear Reconstruction.

Tópicos

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{ take(945) account(800) differ(722) }
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Resumo

We consider the active learning problem, which aims to select the most representative points. Out of many existing active learning techniques, optimum experimental design (OED) has received considerable attention recently. The typical OED criteria minimize the variance of the parameter estimates or predicted value. However, these methods see only global euclidean structure, while the local manifold structure is ignored. For example, I-optimal design selects those data points such that other data points can be best approximated by linear combinations of all the selected points. In this paper, we propose a novel active learning algorithm which takes into account the local structure of the data space. That is, each data point should be approximated by the linear combination of only its neighbors. Given the local reconstruction coefficients for every data point and the coordinates of the selected points, a transductive learning algorithm called Locally Linear Reconstruction (LLR) is proposed to reconstruct every other point. The most representative points are thus defined as those whose coordinates can be used to best reconstruct the whole data set. The sequential and convex optimization schemes are also introduced to solve the optimization problem. The experimental results have demonstrated the effectiveness of our proposed method.

Resumo Limpo

consid activ learn problem aim select repres point mani exist activ learn techniqu optimum experiment design o receiv consider attent recent typic o criteria minim varianc paramet estim predict valu howev method see global euclidean structur local manifold structur ignor exampl ioptim design select data point data point can best approxim linear combin select point paper propos novel activ learn algorithm take account local structur data space data point approxim linear combin neighbor given local reconstruct coeffici everi data point coordin select point transduct learn algorithm call local linear reconstruct llr propos reconstruct everi point repres point thus defin whose coordin can use best reconstruct whole data set sequenti convex optim scheme also introduc solv optim problem experiment result demonstr effect propos method

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