Journal ArticleDOI
Beyond Trace Ratio: Weighted Harmonic Mean of Trace Ratios for Multiclass Discriminant Analysis
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TLDR
A new criterion to maximize the weighted harmonic mean of trace ratios is proposed, which effectively avoid the domination problem while did not raise any difficulties in the formulation and consistently outperforms other compared methods on all of the datasets.Abstract:
Linear discriminant analysis (LDA) is one of the most important supervised linear dimensional reduction techniques which seeks to learn low-dimensional representation from the original high-dimensional feature space through a transformation matrix, while preserving the discriminative information via maximizing the between-class scatter matrix and minimizing the within class scatter matrix. However, the conventional LDA is formulated to maximize the arithmetic mean of trace ratios which suffers from the domination of the largest objectives and might deteriorate the recognition accuracy in practical applications with a large number of classes. In this paper, we propose a new criterion to maximize the weighted harmonic mean of trace ratios, which effectively avoid the domination problem while did not raise any difficulties in the formulation. An efficient algorithm is exploited to solve the proposed challenging problems with fast convergence, which might always find the globally optimal solution just using eigenvalue decomposition in each iteration. Finally, we conduct extensive experiments to illustrate the effectiveness and superiority of our method over both of synthetic datasets and real-life datasets for various tasks, including face recognition, human motion recognition and head pose recognition. The experimental results indicate that our algorithm consistently outperforms other compared methods on all of the datasets.read more
Citations
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Security, privacy and trust of different layers in Internet-of-Things (IoTs) framework
Aakanksha Tewari,Brij B. Gupta +1 more
TL;DR: This paper compares security issues between IoT and traditional network, and discusses opening security issues of IoT, and analyzes the cross-layer heterogeneous integration issues and security issues in detail.
Journal ArticleDOI
Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network
TL;DR: This work reconstructs the high-dimensional features of Android applications (apps) and employ multiple CNN to detect Android malware and proposes a hybrid model based on deep autoencoder (DAE) and convolutional neural network (CNN), which shows powerful ability in feature extraction and malware detection.
Journal ArticleDOI
Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy
Xiong Luo,Jiankun Sun,Long Wang,Weiping Wang,Wenbing Zhao,Jinsong Wu,Jenq-Haur Wang,Zijun Zhang +7 more
TL;DR: An enhanced SELM is developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance.
Journal ArticleDOI
Visual saliency guided complex image retrieval
TL;DR: A novel visual saliency guided complex image retrieval model is proposed which addresses the complexity of the images and proposes a two-stage definition: Cognitive load based complexity and Cognitive level of complexity classification.
Journal ArticleDOI
A Survey of Collaborative Filtering-Based Recommender Systems: From Traditional Methods to Hybrid Methods Based on Social Networks
TL;DR: The recent hybrid CF-based recommendation techniques fusing social networks to solve data sparsity and high dimensionality are introduced and provide a novel point of view to improve the performance of RS, thereby presenting a useful resource in the state-of-the-art research result for future researchers.
References
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Book
Convex Optimization
Stephen Boyd,Lieven Vandenberghe +1 more
TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
Journal ArticleDOI
From few to many: illumination cone models for face recognition under variable lighting and pose
TL;DR: A generative appearance-based method for recognizing human faces under variation in lighting and viewpoint that exploits the fact that the set of images of an object in fixed pose but under all possible illumination conditions, is a convex cone in the space of images.