Large margin metric learning for multi-label prediction

Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Canonical correlation analysis (CCA) and maximum margin output coding (MMOC) methods have shown promising results for multi-label prediction, where each instance is associated with multiple labels. However, these methods require an expensive decoding procedure to recover the multiple labels of each testing instance. The testing complexity becomes unacceptable when there are many labels. To avoid decoding completely, we present a novel large margin metric learning paradigm for multi-label prediction. In particular, the proposed method learns a distance metric to discover label dependency such that instances with very different multiple labels will be moved far away. To handle many labels, we present an accelerated proximal gradient procedure to speed up the learning process. Comprehensive experiments demonstrate that our proposed method is significantly faster than CCA and MMOC in terms of both training and testing complexities. Moreover, our method achieves superior prediction performance compared with state-of-the-art methods

Supervised Machine Learning Techniques for Trojan Detection with Ring Oscillator Network

With the globalization of the semiconductor manufacturing process, electronic devices are powerless against malicious modification of hardware in the supply chain. The ever-increasing threat of hardware Trojan attacks against integrated circuits has spurred a need for accurate and efficient detection methods. Ring oscillator network (RON) is used to detect the Trojan by capturing the difference in power consumption; the power consumption of a Trojan-free circuit is different from the Trojan-inserted circuit. However, the process variation and measurement noise are the major obstacles to detect hardware Trojan with high accuracy. In this paper, we quantitatively compare four supervised machine learning algorithms and classifier optimization strategies for maximizing accuracy and minimizing the false positive rate (FPR). These supervised learning techniques show an improved false positive rate compared to principal component analysis (PCA) and convex hull classification by nearly 40% while maintaining > 90\% binary classification accuracy

Chemometrics approach to FT-IR hyperspectral imaging analysis of degradation products in artwork cross-section

Ascertain the distribution of materials and that of their degradation products in historical artifacts is crucial to understand their conservation status. Among the different analytical techniques that can be used, FT-IR imaging supplies information on the molecular composition of the material on a micrometric-scale in a nondestructive way (i.e. respecting the physical integrity of the material/object and without inducing visible damage to the object. This is possible by limiting the sampling to very small amounts.) (K.H.A. Janssens, R. van Grieken, Non-destructive microanalysis of cultural heritage materials, Elsevier, 2004). When thin sections of the material are not exploitable for transmission, and when ATR imaging mode is not suitable due to possible damages on the sample surface, FT-IR imaging is performed in reflection mode on thick polished, matrix embedded samples. Even if many efforts have been done in the optimization of the sample preparation, the material's surface quality is a critical issue that can hinder the achievement of good infrared images. Moreover, spectral artifacts due to volume and surface interactions can yield uncertain results in standard data treatment. In this paper we address a multivariate statistical analysis as an alternative and complementary approach to obtain high contrast FT-IR large images from hyperspectral data obtained by reflection μ-FTIR analysis. While applications of Principal Component Analysis (PCA) for chemical mapping is well established, no clustering unsupervised method applied to μ-FTIR data have been reported so far in the field of analytical chemistry for cultural heritage. In order to obtain certain chemical distribution of the stratigraphy materials, in this work the use of Hierarchical Cluster Analysis (HCA), validated with a supervised Principal Component based k-Nearest Neighbor (PCA-kNN) Analysis, has been successfully used for the re-construction of the μ-FTIR image, extracting useful information from the complex data set. A case study (a patina from the Arch of Septimius Severus in the Roman Forum) is presented to validate the model and to show new perspectives for FT-IR imaging in art conservation

Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends

Machine learning techniques will contribution towards making Internet of Things (IoT) symmetric applications among the most significant sources of new data in the future. In this context, network systems are endowed with the capacity to access varieties of experimental symmetric data across a plethora of network devices, study the data information, obtain knowledge, and make informed decisions based on the dataset at its disposal. This study is limited to supervised and unsupervised machine learning (ML) techniques, regarded as the bedrock of the IoT smart data analysis. This study includes reviews and discussions of substantial issues related to supervised and unsupervised machine learning techniques, highlighting the advantages and limitations of each algorithm, and discusses the research trends and recommendations for further study

Active learning via query synthesis and nearest neighbour search

Active learning has received great interests from researchers due to its ability to reduce the amount of supervision required for effective learning. As the core component of active learning algorithms, query synthesis and pool-based sampling are two main scenarios of querying considered in the literature. Query synthesis features low querying time, but only has limited applications as the synthesized query might be unrecognizable to human oracle. As a result, most efforts have focused on pool-based sampling in recent years, although it is much more time-consuming. In this paper, we propose new strategies for a novel querying framework that combines query synthesis and pool-based sampling. It overcomes the limitation of query synthesis, and has the advantage of fast querying. The basic idea is to synthesize an instance close to the decision boundary using labelled data, and then select the real instance closest to the synthesized one as a query. For this purpose, we propose a synthesis strategy, which can synthesize instances close to the decision boundary and spreading along the decision boundary. Since the synthesis only depends on the relatively small labelled set, instead of evaluating the entire unlabelled set as many other active learning algorithms do, our method has the advantage of efficiency. In order to handle more complicated data and make our framework compatible with powerful kernel-based learners, we also extend our method to kernel version. Experiments on several real-world data sets show that our method has significant advantage on time complexity and similar performance compared to pool-based uncertainty sampling methods