@inproceedings{8b055917e9f043508a433fbfc13cb8be,
title = "Multi-instance dimensionality reduction",
abstract = "Multi-instance learning deals with problems that treat bags of instances as training examples. In single-instance learning problems, dimensionality reduction is an essential step for high-dimensional data analysis and has been studied for years. The curse of dimensionality also exists in multi-instance learning tasks, yet this difficult task has not been studied before. Direct application of existing single-instance dimensionality reduction objectives to multi-instance learning tasks may not work well since it ignores the characteristic of multi-instance learning that the labels of bags are known while the labels of instances are unknown. In this paper, we propose an effective model and develop an efficient algorithm to solve the multi-instance dimensionality reduction problem. We formulate the objective as an optimization problem by considering orthonormality and sparsity constraints in the projection matrix for dimensionality reduction, and then solve it by the gradient descent along the tangent space of the orthonormal matrices. We also propose an approximation for improving the efficiency. Experimental results validate the effectiveness of the proposed method.",
author = "Sun, {Yu Yin} and NG, {Kwok Po} and Zhou, {Zhi Hua}",
note = "Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10 ; Conference date: 11-07-2010 Through 15-07-2010",
year = "2010",
month = jul,
language = "English",
isbn = "9781577354642",
series = "Proceedings of the National Conference on Artificial Intelligence",
publisher = "AAAI press",
pages = "587--592",
booktitle = "AAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference",
}