@article {48, title = {Multitask Classification Hypothesis Space With Improved Generalization Bounds}, journal = {Neural Networks and Learning Systems, IEEE Transactions on}, volume = {26}, year = {2015}, pages = {1468-1479}, abstract = {This paper presents a pair of hypothesis spaces (HSs) of vector-valued functions intended to be used in the context of multitask classification. While both are parameterized on the elements of reproducing kernel Hilbert spaces and impose a feature mapping that is common to all tasks, one of them assumes this mapping as fixed, while the more general one learns the mapping via multiple kernel learning. For these new HSs, empirical Rademacher complexity-based generalization bounds are derived, and are shown to be tighter than the bound of a particular HS, which has appeared recently in the literature, leading to improved performance. As a matter of fact, the latter HS is shown to be a special case of ours. Based on an equivalence to Group-Lasso type HSs, the proposed HSs are utilized toward corresponding support vector machine-based formulations. Finally, experimental results on multitask learning problems underline the quality of the derived and validate this paper{\textquoteright}s analysis.}, keywords = {Context, Hilbert space, Kernel, Learning systems, Machine learning, pattern recognition, statistical learning, supervised learning, Support vector machines, Training, Upper bound}, issn = {2162-237X}, doi = {10.1109/TNNLS.2014.2347054}, author = {Cong Li and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @article {30, title = {Pareto-Path Multitask Multiple Kernel Learning}, journal = {Neural Networks and Learning Systems, IEEE Transactions on}, volume = {26}, year = {2015}, pages = {51-61}, chapter = {51}, abstract = {

A traditional and intuitively appealing Multitask Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing among the tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a multiobjective optimization problem, which considers the concurrent optimization of all task objectives involved in the (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using the algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches.

}, keywords = {Machine learning, optimization methods, pattern recognition, supervised learning, support vector machines (SVM).}, issn = {2162-237X}, doi = {10.1109/TNNLS.2014.2309939}, author = {Cong Li and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @conference {34, title = {Conic Multi-task Classification}, booktitle = {Machine Learning and Knowledge Discovery in Databases - ECML/PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part II}, year = {2014}, note = {

Acceptance rate 23.8\% (115/483).

}, publisher = {Springer}, organization = {Springer}, address = {Nancy, France}, abstract = {

Traditionally, Multi-task Learning (MTL) models optimize the average of task-related objective functions, which is an intuitive approach and which we will be referring to as Average MTL. However, a more general framework, referred to as Conic MTL, can be formulated by considering conic combinations of the objective functions instead; in this framework, Average MTL arises as a special case, when all combination coefficients equal 1. Although the advantage of Conic MTL over Average MTL has been shown experimentally in previous works, no theoretical justification has been provided to date. In this paper, we derive a generalization bound for the Conic MTL method, and demonstrate that the tightest bound is not necessarily achieved, when all combination coefficients equal 1; hence, Average MTL may not always be the optimal choice, and it is important to consider Conic MTL. As a byproduct of the generalization bound, it also theoretically explains the good experimental results of previous relevant works. Finally, we propose a new Conic MTL model, whose conic combination coefficients minimize the generalization bound, instead of choosing them heuristically as has been done in previous methods. The rationale and advantage of our model is demonstrated and verified via a series of experiments by comparing with several other methods.

}, isbn = {978-3-662-44850-2}, doi = {10.1007/978-3-662-44851-9_13}, url = {http://dx.doi.org/10.1007/978-3-662-44851-9_13}, author = {Cong Li and Michael Georgiopoulos and Georgios C. Anagnostopoulos}, editor = {Toon Calders and Floriana Esposito and Eyke H{\"u}llermeier and Rosa Meo} } @conference {36, title = {Kernel-based Distance Metric Learning in the Output Space}, booktitle = {Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN)}, year = {2013}, note = {

Nominated for Best Paper Award.

Acceptance rate 72\% (435/605).

}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, organization = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {Dallas, Texas, USA}, abstract = {

In this paper we present two related, kernel-based Distance Metric Learning (DML) methods. Their respective models non-linearly map data from their original space to an output space, and subsequent distance measurements are performed in the output space via a Mahalanobis metric. The dimensionality of the output space can be directly controlled to facilitate the learning of a low-rank metric. Both methods allow for simultaneous inference of the associated metric and the mapping to the output space, which can be used to visualize the data, when the output space is 2-or 3-dimensional. Experimental results for a collection of classification tasks illustrate the advantages of the proposed methods over other traditional and kernel-based DML approaches.

}, doi = {10.1109/IJCNN.2013.6706862}, author = {Cong Li and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @conference {37, title = {Reduced-Rank Local Distance Metric Learning}, booktitle = {Machine Learning and Knowledge Discovery in Databases - ECML/PKDD 2013}, year = {2013}, note = {

Acceptance rate 25\% (111/443).

}, publisher = {Springer}, organization = {Springer}, address = {Prague, Czech Republic}, abstract = {

We propose a new method for local metric learning based on a conical combination of Mahalanobis metrics and pair-wise similarities between the data. Its formulation allows for controlling the rank of the metrics{\textquoteright} weight matrices. We also offer a convergent algorithm for training the associated model. Experimental results on a collection of classification problems imply that the new method may offer notable performance advantages over alternative metric learning approaches that have recently appeared in the literature.

}, doi = {10.1007/978-3-642-40994-3_15}, author = {Yinjie Huang and Cong Li and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @article {31, title = {A Unifying Framework for Typical Multitask Multiple Kernel Learning Problems}, journal = {Neural Networks and Learning Systems, IEEE Transactions on}, volume = {25}, year = {2013}, month = {July}, pages = {1287-1297}, abstract = {

Over the past few years, multiple kernel learning (MKL) has received significant attention among data-driven feature selection techniques in the context of kernel-based learning. MKL formulations have been devised and solved for a broad spectrum of machine learning problems, including multitask learning (MTL). Solving different MKL formulations usually involves designing algorithms that are tailored to the problem at hand, which is, typically, a nontrivial accomplishment. In this paper we present a general multitask multiple kernel learning (MT-MKL) framework that subsumes well-known MT-MKL formulations, as well as several important MKL approaches on single-task problems. We then derive a simple algorithm that can solve the unifying framework. To demonstrate the flexibility of the proposed framework, we formulate a new learning problem, namely partially-shared common space MT-MKL, and demonstrate its merits through experimentation.

}, keywords = {Algorithm design and analysis, Closed-form solutions, Kernel, Learning systems, Machine learning, Optimization, optimization methods, pattern recognition, supervised learning, Support vector machines, support vector machines (SVMs), support vector machines (SVMs)., Vectors}, issn = {2162-237X}, doi = {10.1109/TNNLS.2013.2291772}, author = {Cong Li and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @conference {39, title = {Kernel principal subspace Mahalanobis distances for outlier detection}, booktitle = {Neural Networks (IJCNN), The 2011 International Joint Conference on}, year = {2011}, note = {

Acceptance rate 75\% (468/620).

}, month = {July}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, organization = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {San Jose, California, USA }, abstract = {

Over the last few years, Kernel Principal Component Analysis (KPCA) has found several applications in outlier detection. A relatively recent method uses KPCA to compute the reconstruction error (RE) of previously unseen samples and, via thresholding, to identify atypical samples. In this paper we propose an alternative method, which performs the same task, but considers Mahalanobis distances in the orthogonal complement of the subspace that is utilized to compute the reconstruction error. In order to illustrate its merits, we provide qualitative and quantitative results on both artificial and real datasets and we show that it is competitive, if not superior, for several outlier detection tasks, when compared to the original RE-based variant and the One-Class SVM detection approach.

}, keywords = {Image reconstruction, Kernel, kernel principal component analysis, Mahalanobis distance, Manifolds, orthogonal subspace complement, outlier detection, principal component analysis, reconstruction error, Single photon emission computed tomography, Support vector machines, Training}, doi = {10.1109/IJCNN.2011.6033548}, author = {Cong Li and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @article {46, title = {Learning in the Feed-forward Random Neural Network: A Critical Review}, journal = {Perform. Eval.}, volume = {68}, year = {2011}, pages = {361{\textendash}384}, keywords = {ART, CART, Error functions, Evolutionary neural networks, Gradient descent, Learning, Multi-layer perceptron, Multi-objective optimization, Random neural network, SVM}, issn = {0166-5316}, doi = {10.1016/j.peva.2010.07.006}, url = {http://dx.doi.org/10.1016/j.peva.2010.07.006}, author = {Michael Georgiopoulos and Cong Li and Taskin Kocak} }