Implicitly Constrained Semi-Supervised Linear Discriminant Analysis

Semi-supervised learning is an important and active topic of research in pattern recognition. For classification using linear discriminant analysis specifically, several semi-supervised variants have been proposed. Using any one of these methods is not guaranteed to outperform the supervised classifier which does not take the additional unlabeled data into account. In this work we compare traditional Expectation Maximization type approaches for semi-supervised linear discriminant analysis with approaches based on intrinsic constraints and propose a new principled approach for semi-supervised linear discriminant analysis, using so-called implicit constraints. We explore the relationships between these methods and consider the question if and in what sense we can expect improvement in performance over the supervised procedure. The constraint based approaches are more robust to misspecification of the model, and may outperform alternatives that make more assumptions on the data, in terms of the log-likelihood of unseen objects.Comment: 6 pages, 3 figures and 3 tables. International Conference on Pattern Recognition (ICPR) 2014, Stockholm, Swede

Robust semi-supervised learning: projections, limits & constraints

In many domains of science and society, the amount of data being gathered is increasing rapidly. To estimate input-output relationships that are often of interest, supervised learning techniques rely on a specific type of data: labeled examples for which we know both the input and an outcome. The problem of semi-supervised learning is how to use, increasingly abundantly available, unlabeled examples, with unknown outcomes, to improve supervised learning methods. This thesis is concerned with the question if and how these improvements are possible in a "robust", or safe, way: can we guarantee these methods do not lead to worse performance than the supervised solution?We show that for some supervised classifiers, most notably, the least squares classifier, semi-supervised adaptations can be constructed where this non-degradation in performance can indeed be guaranteed, in terms of the surrogate loss used by the classifier. Since these guarantees are given in terms of the surrogate loss, we explore why this is a useful criterion to evaluate performance. We then prove that semi-supervised versions with strict non-degradation guarantees are not possible for a large class of commonly used supervised classifiers. Other aspects covered in the thesis include optimistic learning, the peaking phenomenon and reproducibility.COMMIT - Project P23LUMC / Geneeskunde Repositoriu

On-line quality monitoring and lifetime prediction of thick Al wire bonds using signals obtained from ultrasonic generator

Abstract The reliable performance of power electronic modules has been a concern for many years due to their increased use in applications which demand high availability and longer lifetimes. Thick Al wire bonding is a key technique for providing interconnections in power electronic modules. Today, wire bond lift-off and heel cracking are often considered the most lifetime limiting factors of power electronic modules as a result of cyclic thermomechanical stresses. Therefore, it is important for power electronic packaging manufacturers to address this issue at the design stage and on the manufacturing line. Techniques for the non-destructive, real-time evaluation and control of wire bond quality have been proposed to detect defects in manufacture and predict reliability prior to in-service exposure. This approach has the potential to improve the accuracy of lifetime prediction for the manufactured product. In this thesis, a non-destructive technique for detecting bond quality by the application of a semi-supervised classification algorithm to process signals obtained from an ultrasonic generator is presented. Experimental tests verified that the classification method is capable of accurately predicting bond quality, indicated by bonded area as measured by X-ray tomography. Samples classified during bonding were subjected to both passive and active cycling and the distribution of bond life amongst the different classes analysed. It is demonstrated that the as-bonded quality classification is closely correlated with cycling life and can therefore be used as a non-destructive tool for monitoring bond quality and predicting useful service life

On-line quality monitoring and lifetime prediction of thick Al wire bonds using signals obtained from ultrasonic generator

Abstract The reliable performance of power electronic modules has been a concern for many years due to their increased use in applications which demand high availability and longer lifetimes. Thick Al wire bonding is a key technique for providing interconnections in power electronic modules. Today, wire bond lift-off and heel cracking are often considered the most lifetime limiting factors of power electronic modules as a result of cyclic thermomechanical stresses. Therefore, it is important for power electronic packaging manufacturers to address this issue at the design stage and on the manufacturing line. Techniques for the non-destructive, real-time evaluation and control of wire bond quality have been proposed to detect defects in manufacture and predict reliability prior to in-service exposure. This approach has the potential to improve the accuracy of lifetime prediction for the manufactured product. In this thesis, a non-destructive technique for detecting bond quality by the application of a semi-supervised classification algorithm to process signals obtained from an ultrasonic generator is presented. Experimental tests verified that the classification method is capable of accurately predicting bond quality, indicated by bonded area as measured by X-ray tomography. Samples classified during bonding were subjected to both passive and active cycling and the distribution of bond life amongst the different classes analysed. It is demonstrated that the as-bonded quality classification is closely correlated with cycling life and can therefore be used as a non-destructive tool for monitoring bond quality and predicting useful service life