Transfer learning focuses on the reuse of supervised learning models in a new context. Prominent applications can be found in robotics, image processing or web mining. In these areas, learning scenarios change by nature, but often remain related and motivate the reuse of existing supervised models. While the majority of symmetric and asymmetric domain adaptation algorithms utilize all available source and target domain data, we show that domain adaptation requires only a substantial smaller subset. This makes it more suitable for real-world scenarios where target domain data is rare. The presented approach finds a target subspace representation for source and target data to address domain differences by orthogonal basis transfer. We employ Nystrom techniques and show the reliability of this approximation without a particular landmark matrix by applying post-transfer normalization. It is evaluated on typical domain adaptation tasks with standard benchmark data.
This book studies the original results, and their extensions, of the Russian mathematician, S.A. Gersgorin, who wrote a seminal paper in 1931, on how to easily obtain estimates of all n eigenvalues (characteristic values) of any given n-by-n complex matrix. Since the publication of this paper, there has been many newer results spawned by his paper, and this book will be the first which is devoted solely to this resulting area. As such, it will include the latest research results, such as Brauer ovals of Cassini and Brualdi lemniscates, and their comparisons. This book is dedicated to the late Olga Taussky-Todd and her husband, John Todd. It was Olga who brought to light Gersgorin's paper and its significance to the mathematical world. The level of this book requires only a modest background in linear algebra and analysis, and is therefore comprehensible to upper-level and graduate level students in mathematics.