@conference {57, title = {Hash Function Learning via Codewords}, booktitle = {Machine Learning and Knowledge Discovery in Databases - ECML/PKDD 2015}, series = {Lecture Notes in Computer Science}, volume = {9284}, year = {2015}, note = {Acceptance rate 23.4\% (89/380).}, pages = {659-674}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {In this paper we introduce a novel hash learning framework that has two main distinguishing features, when compared to past approaches. First, it utilizes codewords in the Hamming space as ancillary means to accomplish its hash learning task. These codewords, which are inferred from the data, attempt to capture similarity aspects of the data{\textquoteright}s hash codes. Secondly and more importantly, the same framework is capable of addressing supervised, unsupervised and, even, semi-supervised hash learning tasks in a natural manner. A series of comparative experiments focused on content-based image retrieval highlights its performance advantages.}, keywords = {Codeword, Hash function learning, support vector machine}, author = {Yinjie Huang 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} } @conference {41, title = {Accelerated learning of Generalized Sammon Mappings}, 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 = {

The Sammon Mapping (SM) has established itself as a valuable tool in dimensionality reduction, manifold learning, exploratory data analysis and, particularly, in data visualization. The SM is capable of projecting high-dimensional data into a low-dimensional space, so that they can be visualized and interpreted. This is accomplished by representing inter-sample dissimilarities in the original space by Euclidean inter-sample distances in the projection space. Recently, Kernel Sammon Mapping (KSM) has been shown to subsume the SM and a few other related extensions to SM. Both of the aforementioned models feature a set of linear weights that are estimated via Iterative Majorization (IM). While IM is significantly faster than other standard gradient-based methods, tackling data sets of larger than moderate sizes becomes a challenging learning task, as IM{\textquoteright}s convergence significantly slows down with increasing data set cardinality. In this paper we derive two improved training algorithms based on Successive Over-Relaxation (SOR) and Parallel Tangents (PARTAN) acceleration, that, while still being first-order methods, exhibit faster convergence than IM. Both algorithms are relatively easy to understand, straightforward to implement and, performance-wise, are as robust as IM. We also present comparative results that illustrate their computational advantages on a set of benchmark problems.

}, keywords = {accelerated learning, Acceleration, Convergence, data visualization, dimensionality reduction, Euclidean inter-sample distances, exploratory data analysis, generalized Sammon mappings, inter-sample dissimilarities, iterative majorization, Kernel, kernel Sammon mapping, learning (artificial intelligence), manifold learning, parallel tangents acceleration, projection space, Prototypes, standard gradient-based methods, Stress, successive over-relaxation, Training}, doi = {10.1109/IJCNN.2011.6033609}, author = {Yinjie Huang and Michael Georgiopoulos and Georgios C. Anagnostopoulos} }