@conference {58,
	title = {Multi-Task Learning with Group-Specific Feature Space Sharing},
	booktitle = {Machine Learning and Knowledge Discovery in Databases - ECML/PKDD 2015},
	series = {Lecture Notes in Computer Science},
	volume = {9285},
	year = {2015},
	note = {Acceptance rate 23.4\% (89/380).},
	pages = {120-136},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {When faced with learning a set of inter-related tasks from a limited amount of usable data, learning each task independently may lead to poor generalization performance. (MTL) exploits the latent relations between tasks and overcomes data scarcity limitations by co-learning all these tasks simultaneously to offer improved performance. We propose a novel Multi-Task Multiple Kernel Learning framework based on Support Vector Machines for binary classification tasks. By considering pair-wise task affinity in terms of similarity between a pair{\textquoteright}s respective feature spaces, the new framework, compared to other similar MTL approaches, offers a high degree of flexibility in determining how similar feature spaces should be, as well as which pairs of tasks should share a common feature space in order to benefit overall performance. The associated optimization problem is solved via a block coordinate descent, which employs a consensus-form Alternating Direction Method of Multipliers algorithm to optimize the Multiple Kernel Learning weights and, hence, to determine task affinities. Empirical evaluation on seven data sets exhibits a statistically significant improvement of our framework{\textquoteright}s results compared to the ones of several other Clustered Multi-Task Learning methods.},
	doi = {10.1007/978-3-319-23525-7_8},
	author = {Niloofar Yousefi and Michael Georgiopoulos and Georgios C. Anagnostopoulos}
}
@conference {42,
	title = {Metric representations of data via the Kernel-based Sammon Mapping},
	booktitle = {Neural Networks (IJCNN), The 2010 International Joint Conference on},
	year = {2010},
	note = {<p>Acceptance rate 71.5\% (621/868).</p>
},
	month = {July},
	publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
	organization = {Institute of Electrical and Electronics Engineers (IEEE)},
	address = {Barcelona, Spain},
	abstract = {<p>In this paper we present a novel generalization of Sammon{\textquoteright}s Mapping (SM), which is a popular, metric multi-dimensional scaling technique used in data analysis and visualization. The new approach, namely the Kernel-based Sammon Mapping (KSM), yields the classic SM and other much related techniques as special cases. Apart from being able to approximate distance-preserving projections, it can also learn to metrically represent arbitrarily-defined dissimilarities or similarities between samples. Moreover, it can handle equally well numeric, categorical or mixed-type data. It is able to accomplish all this by modeling its projections as linear combinations of appropriate kernel functions. We report experimental results, which showcase KSM{\textquoteright}s capabilities in visually representing several meaningful relationships between samples of selected datasets.</p>
},
	keywords = {data analysis, data visualisation, data visualization, distance-preserving projections, kernel-based Sammon mapping, metric multidimensional scaling technique, metric representations, Visualization},
	doi = {10.1109/IJCNN.2010.5596662},
	author = {Mingbo Ma and Ryan Gonet and RuiZhi Yu and Georgios C. Anagnostopoulos}
}
