Nonlinear Supervised Dimensionality Reduction via Smooth Regular Embeddings

The recovery of the intrinsic geometric structures of data collections is an important problem in data analysis. Supervised extensions of several manifold learning approaches have been proposed in the recent years. Meanwhile, existing methods primarily focus on the embedding of the training data, and the generalization of the embedding to initially unseen test data is rather ignored. In this work, we build on recent theoretical results on the generalization performance of supervised manifold learning algorithms. Motivated by these performance bounds, we propose a supervised manifold learning method that computes a nonlinear embedding while constructing a smooth and regular interpolation function that extends the embedding to the whole data space in order to achieve satisfactory generalization. The embedding and the interpolator are jointly learnt such that the Lipschitz regularity of the interpolator is imposed while ensuring the separation between different classes. Experimental results on several image data sets show that the proposed method outperforms traditional classifiers and the supervised dimensionality reduction algorithms in comparison in terms of classification accuracy in most settings

GENERALIZABLE SUPERVISED MANIFOLD LEARNING VIA LIPSCHITZ CONTINUOUS INTERPOLATORS

Many supervised dimensionality reduction methods have been proposed in the recent years. Linear manifold learning methods often have limited flexibility in learning effective representations, whereas nonlinear methods mainly focus on the embedding of the training samples and do not consider the performance of the generalization of the embedding to initially unseen test samples. In this paper, we build on recent theoretical results on the generalization performance of supervised manifold learners, which state that in order to achieve good generalization performance, a trade-off needs to be sought between the separation of different classes in the embedding and the possibility of constructing out-of-sample interpolators with good Lipschitz regularity. In the light of these results, we propose a new supervised manifold learning algorithm that computes an embedding of the training samples along with a smooth interpolation function generalizing the embedding to the whole space. Our method is based on a learning objective that explicitly takes into account the generalization performance to novel test samples. Experimental results show that the proposed method achieves high classification accuracy in comparison with state-of-the-art supervised manifold learning algorithms

Atmospheric-Induced Stress Corrosion Cracking of Grade 2205 Duplex Stainless Steel — Effects of 475C Embrittlement and Process Orientation

The effect of 475 °C embrittlement and microstructure process orientation on atmospheric-induced stress corrosion cracking (AISCC) of grade 2205 duplex stainless steel has been investigated. AISCC tests were carried out under salt-laden, chloride-containing deposits, on U-bend samples manufactured in rolling (RD) and transverse directions (TD). The occurrence of selective corrosion and stress corrosion cracking was observed, with samples in TD displaying higher propensity towards AISCC. Strains and tensile stresses were observed in both ferrite and austenite, with similar magnitudes in TD, whereas, larger strains and stresses in austenite in RD. The occurrence of 475 °C embrittlement was related to microstructural changes in the ferrite. Exposure to 475 °C heat treatment for 5 to 10 h resulted in better AISCC resistance, with spinodal decomposition believed to enhance the corrosion properties of the ferrite. The austenite was more susceptible to ageing treatments up to 50 h, with the ferrite becoming more susceptible with ageing in excess of 50 h. Increased susceptibility of the ferrite may be related to the formation of additional precipitates, such as R-phase. The implications of heat treatment at 475 °C and the effect of process orientation are discussed in light of microstructure development and propensity to AISCC

Corrosion Inhibition of Aluminum Alloy AA6063-T5 by Vanadates: Microstructure Characterization and Corrosion Analysis

Было исследовано ингибирование коррозии алюминиевого сплава AA6063-T5 ванадатами (NaVO[3]) в 0,05 М растворе NaCl с помощью электрохимических измерений и измерений потери веса, и связано с данными микроструктуры и вольт-потенциала