Name
Abstract | ||
|---|---|---|
The multiple instance regression (MIR) problem arises when a data set is a collection of bags, where each bag contains multiple instances sharing the identical real-valued label. The goal is to train a regression model that can accurately predict label of an unlabeled bag. Many remote sensing applications can be studied within this setting. We propose a novel probabilistic framework for MIR that represents bag labels with a mixture model. It is based on an assumption that each bag contains a prime instance which is responsible for the bag label. An expectation-maximization algorithm is proposed to maximize the likelihood of the mixture model. The mixture model MIR framework is quite flexible, and several existing MIR algorithms can be described as its special cases. The proposed algorithms were evaluated on synthetic data and remote sensing data for aerosol retrieval and crop yield prediction. The results show that the proposed MIR algorithms achieve higher accuracy than the previous state of the art. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TGRS.2011.2171691 | IEEE T. Geoscience and Remote Sensing |
Keywords | Field | DocType |
multiple instance regression (mir),geophysical techniques,remote sensing,mixture model mir framework,neural networks,identical real-valued label,expectation-maximization algorithm,aerosol retrieval,multiple instance learning (mil),regression analysis,maximum likelihood estimation,moderate resolution imaging spectroradiometer (modis),remote sensing data,multiple instance regression mixture model,expectation maximization,aerosols,crop yield prediction,multiple instance regression problem,crops,synthetic data,mixture model likelihood analysis,probabilistic framework,mixture model,multi-angle imaging spectroradiometer (misr),aerosol retrieval analysis,probability,accuracy,neural network,ocean temperature,crop yield,predictive models,agriculture,regression model,expectation maximization algorithm,prediction model,prediction algorithms | Prime (order theory),Data mining,Regression,Regression analysis,Expectation–maximization algorithm,Remote sensing,Remote sensing application,Synthetic data,Artificial neural network,Mathematics,Mixture model | Journal |
Volume | Issue | ISSN |
50 | 6 | 0196-2892 |
Citations | PageRank | References |
2 | 0.41 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhuang Wang | 1 | 246 | 15.35 |
| Liang Lan | 2 | 138 | 9.86 |
| Slobodan Vucetic | 3 | 637 | 56.38 |