72 research outputs found
Domain Adaptation Extreme Learning Machines for Drift Compensation in E-nose Systems
This paper addresses an important issue, known as sensor drift that behaves a
nonlinear dynamic property in electronic nose (E-nose), from the viewpoint of
machine learning. Traditional methods for drift compensation are laborious and
costly due to the frequent acquisition and labeling process for gases samples
recalibration. Extreme learning machines (ELMs) have been confirmed to be
efficient and effective learning techniques for pattern recognition and
regression. However, ELMs primarily focus on the supervised, semi-supervised
and unsupervised learning problems in single domain (i.e. source domain). To
our best knowledge, ELM with cross-domain learning capability has never been
studied. This paper proposes a unified framework, referred to as Domain
Adaptation Extreme Learning Machine (DAELM), which learns a robust classifier
by leveraging a limited number of labeled data from target domain for drift
compensation as well as gases recognition in E-nose systems, without loss of
the computational efficiency and learning ability of traditional ELM. In the
unified framework, two algorithms called DAELM-S and DAELM-T are proposed for
the purpose of this paper, respectively. In order to percept the differences
among ELM, DAELM-S and DAELM-T, two remarks are provided. Experiments on the
popular sensor drift data with multiple batches collected by E-nose system
clearly demonstrate that the proposed DAELM significantly outperforms existing
drift compensation methods without cumbersome measures, and also bring new
perspectives for ELM.Comment: 11 pages, 9 figures, to appear in IEEE Transactions on
Instrumentation and Measuremen
Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification
Stacking-based deep neural network (S-DNN) is aggregated with pluralities of
basic learning modules, one after another, to synthesize a deep neural network
(DNN) alternative for pattern classification. Contrary to the DNNs trained end
to end by backpropagation (BP), each S-DNN layer, i.e., a self-learnable
module, is to be trained decisively and independently without BP intervention.
In this paper, a ridge regression-based S-DNN, dubbed deep analytic network
(DAN), along with its kernelization (K-DAN), are devised for multilayer feature
re-learning from the pre-extracted baseline features and the structured
features. Our theoretical formulation demonstrates that DAN/K-DAN re-learn by
perturbing the intra/inter-class variations, apart from diminishing the
prediction errors. We scrutinize the DAN/K-DAN performance for pattern
classification on datasets of varying domains - faces, handwritten digits,
generic objects, to name a few. Unlike the typical BP-optimized DNNs to be
trained from gigantic datasets by GPU, we disclose that DAN/K-DAN are trainable
using only CPU even for small-scale training sets. Our experimental results
disclose that DAN/K-DAN outperform the present S-DNNs and also the BP-trained
DNNs, including multiplayer perceptron, deep belief network, etc., without data
augmentation applied.Comment: 14 pages, 7 figures, 11 table
Computational intelligent methods for trusting in social networks
104 p.This Thesis covers three research lines of Social Networks. The first proposed reseach line is related with Trust. Different ways of feature extraction are proposed for Trust Prediction comparing results with classic methods. The problem of bad balanced datasets is covered in this work. The second proposed reseach line is related with Recommendation Systems. Two experiments are proposed in this work. The first experiment is about recipe generation with a bread machine. The second experiment is about product generation based on rating given by users. The third research line is related with Influence Maximization. In this work a new heuristic method is proposed to give the minimal set of nodes that maximizes the influence of the network
Cross-position Activity Recognition with Stratified Transfer Learning
Human activity recognition aims to recognize the activities of daily living
by utilizing the sensors on different body parts. However, when the labeled
data from a certain body position (i.e. target domain) is missing, how to
leverage the data from other positions (i.e. source domain) to help learn the
activity labels of this position? When there are several source domains
available, it is often difficult to select the most similar source domain to
the target domain. With the selected source domain, we need to perform accurate
knowledge transfer between domains. Existing methods only learn the global
distance between domains while ignoring the local property. In this paper, we
propose a \textit{Stratified Transfer Learning} (STL) framework to perform both
source domain selection and knowledge transfer. STL is based on our proposed
\textit{Stratified} distance to capture the local property of domains. STL
consists of two components: Stratified Domain Selection (STL-SDS) can select
the most similar source domain to the target domain; Stratified Activity
Transfer (STL-SAT) is able to perform accurate knowledge transfer. Extensive
experiments on three public activity recognition datasets demonstrate the
superiority of STL. Furthermore, we extensively investigate the performance of
transfer learning across different degrees of similarities and activity levels
between domains. We also discuss the potential applications of STL in other
fields of pervasive computing for future research.Comment: Submit to Pervasive and Mobile Computing as an extension to PerCom 18
paper; First revision. arXiv admin note: substantial text overlap with
arXiv:1801.0082
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