# Show Reference: "Semisupervised Multimodal Dimensionality Reduction"

Semisupervised Multimodal Dimensionality Reduction Computational Intelligence, Vol. 29, No. 1. (1 February 2013), pp. 70-110, doi:10.1111/j.1467-8640.2012.00429.x by Zhao Zhang, Tommy W. S. Chow, Ning Ye
@article{zhang-et-al-2013,
abstract = {The problem of learning from both labeled and unlabeled data is considered. In this paper, we present a novel semisupervised multimodal dimensionality reduction ({SSMDR}) algorithm for feature reduction and extraction. {SSMDR} can preserve the local and multimodal structures of labeled and unlabeled samples. As a result, data pairs in the close vicinity of the original space are projected in the nearby of the embedding space. Due to overfitting, supervised dimensionality reduction methods tend to perform inefficiently when only few labeled samples are available. In such cases, unlabeled samples play a significant role in boosting the learning performance. The proposed discriminant technique has an analytical form of the embedding transformations that can be effectively obtained by applying the eigen decomposition, or finding two close optimal sets of transforming basis vectors. By employing the standard kernel trick, {SSMDR} can be extended to the nonlinear dimensionality reduction scenarios. We verify the feasibility and effectiveness of {SSMDR} through conducting extensive simulations including data visualization and classification on the synthetic and real-world datasets. Our obtained results reveal that {SSMDR} offers significant advantages over some widely used techniques. Compared with other methods, the proposed {SSMDR} exhibits superior performance on multimodal cases.},
author = {Zhang, Zhao and Chow, Tommy W. S. and Ye, Ning},
day = {1},
doi = {10.1111/j.1467-8640.2012.00429.x},
journal = {Computational Intelligence},
keywords = {dimensionality-reduction, multisensory-integration, supervised-learning, unsupervised-learning},
month = feb,
number = {1},
pages = {70--110},
posted-at = {2013-08-13 02:06:58},
priority = {2},
publisher = {Blackwell Publishing Inc},
title = {Semisupervised Multimodal Dimensionality Reduction},
url = {http://dx.doi.org/10.1111/j.1467-8640.2012.00429.x},
volume = {29},
year = {2013}
}