New developments of dimension reduction

Variable selection becomes more crucial than before, since high dimensional data are frequently seen in many research areas. Many model-based variable selection methods have been developed. However, the performance might be poor when the model is mis-specified. Sufficient dimension reduction (SDR, Li 1991; Cook 1998) provides a general framework for model-free variable selection methods. In this thesis, we first propose a novel model-free variable selection method to deal with multi-population data by incorporating the grouping information. Theoretical properties of our proposed method are also presented. Simulation studies show that our new method significantly improves the selection performance compared with those ignoring the grouping information. In the second part of this dissertation, we apply partial SDR method to conduct conditional model-free variable (feature) screening for ultra-high dimensional data, when researchers have prior information regarding the importance of certain predictors based on experience or previous investigations. Comparing to the state of art conditional screening method, conditional sure independence screening (CSIS; Barut, Fan and Verhasselt, 2016), our method greatly outperforms CSIS for nonlinear models. The sure screening consistency property of our proposed method is also established --Abstract, page iv

A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification

Unsupervised cross-domain person re-identification (Re-ID) faces two key issues. One is the data distribution discrepancy between source and target domains, and the other is the lack of labelling information in target domain. They are addressed in this paper from the perspective of representation learning. For the first issue, we highlight the presence of camera-level sub-domains as a unique characteristic of person Re-ID, and develop camera-aware domain adaptation to reduce the discrepancy not only between source and target domains but also across these sub-domains. For the second issue, we exploit the temporal continuity in each camera of target domain to create discriminative information. This is implemented by dynamically generating online triplets within each batch, in order to maximally take advantage of the steadily improved feature representation in training process. Together, the above two methods give rise to a novel unsupervised deep domain adaptation framework for person Re-ID. Experiments and ablation studies on benchmark datasets demonstrate its superiority and interesting properties.Comment: Accepted by ICCV201

Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis

© 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.Bearing vibration signals contain non-linear and non-stationary features due to instantaneous variations in the operation of rotating machinery. It is important to characterize and analyze the complexity change of the bearing vibration signals so that bearing health conditions can be accurately identified. Entropy measures are non-linear indicators that are applicable to the time series complexity analysis for machine fault diagnosis. In this paper, an improved entropy measure, termed Adaptive Multiscale Weighted Permutation Entropy (AMWPE), is proposed. Then, a new rolling bearing fault diagnosis method is developed based on the AMWPE and multi-class SVM. For comparison, experimental bearing data are analyzed using the AMWPE, compared with the conventional entropy measures, where a multi-class SVM is adopted for fault type classification. Moreover, the robustness of different entropy measures is further studied for the analysis of noisy signals with various Signal-to-Noise Ratios (SNRs). The experimental results have demonstrated the effectiveness of the proposed method in fault diagnosis of rolling bearing under different fault types, severity degrees, and SNR levels.Peer reviewedFinal Accepted Versio

Online Deep Metric Learning

Metric learning learns a metric function from training data to calculate the similarity or distance between samples. From the perspective of feature learning, metric learning essentially learns a new feature space by feature transformation (e.g., Mahalanobis distance metric). However, traditional metric learning algorithms are shallow, which just learn one metric space (feature transformation). Can we further learn a better metric space from the learnt metric space? In other words, can we learn metric progressively and nonlinearly like deep learning by just using the existing metric learning algorithms? To this end, we present a hierarchical metric learning scheme and implement an online deep metric learning framework, namely ODML. Specifically, we take one online metric learning algorithm as a metric layer, followed by a nonlinear layer (i.e., ReLU), and then stack these layers modelled after the deep learning. The proposed ODML enjoys some nice properties, indeed can learn metric progressively and performs superiorly on some datasets. Various experiments with different settings have been conducted to verify these properties of the proposed ODML.Comment: 9 page

OPML: A One-Pass Closed-Form Solution for Online Metric Learning

To achieve a low computational cost when performing online metric learning for large-scale data, we present a one-pass closed-form solution namely OPML in this paper. Typically, the proposed OPML first adopts a one-pass triplet construction strategy, which aims to use only a very small number of triplets to approximate the representation ability of whole original triplets obtained by batch-manner methods. Then, OPML employs a closed-form solution to update the metric for new coming samples, which leads to a low space (i.e., $O(d)$) and time (i.e., $O(d^2)$) complexity, where $d$ is the feature dimensionality. In addition, an extension of OPML (namely COPML) is further proposed to enhance the robustness when in real case the first several samples come from the same class (i.e., cold start problem). In the experiments, we have systematically evaluated our methods (OPML and COPML) on three typical tasks, including UCI data classification, face verification, and abnormal event detection in videos, which aims to fully evaluate the proposed methods on different sample number, different feature dimensionalities and different feature extraction ways (i.e., hand-crafted and deeply-learned). The results show that OPML and COPML can obtain the promising performance with a very low computational cost. Also, the effectiveness of COPML under the cold start setting is experimentally verified.Comment: 12 page

How Green Public Procurement Contributes to Sustainable Development in China: Evidence from the IISD Green Public Procurement Model

The People's Republic of China spent more than CNY 1.6 trillion (USD 252 billion) on procurement in 2013, accounting for 11.7 per cent of all national spending (Ministry of Finance of the People's Republic of China, 2014). In light of these numbers, the potential environmental, social and economic multipliers of greening government purchases become evident. The benefits of a comprehensive and efficient green public procurement (GPP) policy are not limited to the green products and services the public sector buys, but will have a ripple effect that encourages green consumption nationwide. The significant purchasing power of the government will provide the much-needed incentives in order for businesses to invest and innovate in green products and services to meet the government's guaranteed long-term and high-volume demand. Additionally, GPP is in line with China's national plans to pioneer "eco-civilisation" and with the upcoming 13th Five-Year Plan (FYP), which underlines the importance of GPP.This paper is the second and final component of IISD's contribution to greening public procurement in China. Our discussion paper Green Public Procurement in China: Quantifying the Benefits, published in April 2015, analyzed China's GPP landscape, taking a closer look at current practices, actors at different levels of government and the underlying legal framework. In addition, the paper introduced the IISD GPP Model, discussing its potential for quantifying and communicating the benefits of GPP, while providing a high-level overview of the modelling approach used and of the scope of the model envisioned. Building on the results of the IISD GPP Model, consultations with stakeholders and an extensive literature review, this paper provides targeted recommendations addressing the development areas identified to improve GPP in China. The recommendations follow a multiphase approach offering more immediate solutions as well as more ambitious, larger-scale overhauls of the GPP framework for the long term. The results of the IISD GPP Model will be shared for the first time as part of this paper, making the case for green procurement through analyzing five product categories: air conditioners, lighting, cars, paper and cement. These categories were selected because they represent significant financial flows in procurement, have notable environmental impacts and domestic production, and have sufficient data available to facilitate their analysis. A detailed overview of the key elements of the modelling approach will be provided, in addition to an explanation of the model setup and the range of externalities monetised for each product category. Finally, we will look at how to use the model at the different levels of government as well as how its scope can be extended and customised in order to leverage its potential under a wider range of circumstances and areas of procurement