4832713
Heteroscedastic Probabilistic Linear Discriminant Analysis with Semi-supervised Extension
Linear discriminant analysis (LDA) is a commonly used method for dimensionality reduction. Despite its successes, it has limitations under some situations, including the small sample size problem, the homoscedasticity assumption that different classes have the same Gaussian distribution, and its inability to produce probabilistic output and handle missing data. In this paper, we propose a semi-supervised and heteroscedastic extension of probabilistic LDA, called S2HPLDA, which aims at overcoming all these limitations under a common principled framework. Moreover, we apply automatic relevance determination to determine the required dimensionality of the low-dimensional space for dimensionality reduction. We empirically compare our method with several related probabilistic subspace methods on some face and object databases. Very promising results are obtained from the experiments showing the effectiveness of our proposed method.
15044843
Learning from facial aging patterns for automatic age estimation
Age Specific Human-Computer Interaction (ASHCI) has vast potential applications in daily life. However, automatic age estimation technique is still underdeveloped. One of the main reasons is that the aging effects on human faces present several unique characteristics which make age estimation a challenging task that requires non-standard classification approaches. According to the speciality of the facial aging effects, this paper proposes the AGES (AGing pattErn Subspace) method for automatic age estimation. The basic idea is to model the aging pattern, which is defined as a sequence of personal aging face images, by learning a representative subspace. The proper aging pattern for an unseen face image is then determined by the projection in the subspace that can best reconstruct the face image, while the position of the face image in that aging pattern will indicate its age. The AGES method has shown encouraging performance in the comparative experiments either as an age estimator or as an age range estimator.
4846997
Semi-supervised Discriminant Analysis Via CCCP
Linear discriminant analysis (LDA) is commonly used for dimensionality reduction. In real-world applications where labeled data are scarce, LDA does not work very well. However, unlabeled data are often available in large quantities. We propose a novel semi-supervised discriminant analysis algorithm called SSDA$_{\mathit{CCCP}}$. We utilize unlabeled data to maximize an optimality criterion of LDA and use the constrained concave-convex procedure to solve the optimization problem. The optimization procedure leads to estimation of the class labels for the unlabeled data. We propose a novel confidence measure for selecting those unlabeled data points with high confidence. The selected unlabeled data can then be used to augment the original labeled data set for performing LDA. We also propose a variant of SSDA$_{\mathit{CCCP}}$, called M-SSDA$_{\mathit{CCCP}}$, which adopts the manifold assumption to utilize the unlabeled data. Extensive experiments on many benchmark data sets demonstrate the effectiveness of our proposed methods.
2385031
Semi-Supervised Multi-Task Regression
Labeled data are needed for many machine learning applications but the amount available in some applications is scarce. Semi-supervised learning and multi-task learning are two of the approaches that have been proposed to alleviate this problem. In this paper, we seek to integrate these two approaches for regression applications. We first propose a new supervised multi-task regression method called SMTR, which is based on Gaussian processes (GP) with the assumption that the kernel parameters for all tasks share a common prior. We then incorporate unlabeled data into SMTR by changing the kernel function of the GP prior to a data-dependent kernel function, resulting in a semi-supervised extension of SMTR, called SSMTR. Moreover, we incorporate pairwise information into SSMTR to further boost the learning performance for applications in which such information is available. Experiments conducted on two commonly used data sets for multi-task regression demonstrate the effectiveness of our methods.
815666
An Efficient Markerless Method for Resynthesizing Facial Animation on an Anatomy-Based Model
This paper describes a markerless method for automatically estimating animation parameters of an anatomy-based face model from a video sequence. The method is based upon an efficient tracking procedure which employs a linear subspace model of facial appearance. During the training phase of the tracking algorithm, a set of templates is computed from the subspace basis to alleviate the online computation. The efficient tracking algorithm establishes temporal correspondence of the face region by simultaneously determining both motion and appearance parameters. Using a set of key expressions of the actor together with corresponding animation parameters of the face model, we learn a linear relationship between facial animation parameters and appearance parameters. It allows us to efficiently estimate the animation parameters from appearance parameters provided by the tracker. We present experimental results performed on synthetic and live video sequences to demonstrate the effectiveness and efficiency of our method.
27484800
Analysis of Mixed Inflammable Gases Based on Single Sensor and RBF Neural Network
The sensitivity of a catalytic sensor changes according to different types of gases or different temperatures. To fully exploit this property, a sensor can be controlled to work under different temperatures to produce different output signals for a given mixture of inflammable gases. An Radial Basis Function (RBF) neural network can be used to analyze the mixture of gases by using a dynamic learning algorithm. The simulation experiment, with a sample mixture of firedamp, carbon monoxide and hydrogen, shows that the proposed method is indeed efficient in analyzing mixtures of inflammable gases.
5679790
Proactive Information Exchange During Team Cooperation
We are concerned, ultimately, with multi-agent teams involving both software and human agents in which the software agents assist in training the humans, e.g., as virtual team members. The software agents must be capable of utilizing the teamwork mechanisms used by humans. In this paper, we describe how to give agents the same implicit communication capabilities, i.e., observation, that humans use. We show how agents can use observations of the environment and teammates' actions to estimate other agents beliefs without bothering them with unnecessary messages; we also show how agents can anticipate information needs among the team and proactively exchange information, reducing the total communication volume. To achieve these goals, we add sensing capabilities to agents and present algorithms that infer teammates' mental state from the observation. Experimental data is presented that shows that the advantage of observation in proactive information exchange as well as enhanced team performance.
41646301
A Comparison of Broadcast Strategy in MIMO Relay Networks
In this paper, the sum rate issue of a multiple-input multiple-output (MIMO) relay broadcast channel is studied, where the fixed relay station is used to support multiuser transmission in a downlink channel. With appropriate design, the sum rate can be expressed as a function of the processing units at the base station (BS) and relay, and the power allocation can be converted to a standard convex problem. We compared the sum rate of MIMO relay broadcast channel (BC) using time division multiple access (TDMA) to the strongest user, dirty paper coding (DPC) and beamforming (BF) when multiple antennas are deployed at each terminal in the system and separate power constraints at the BS and relay. Then, the sum rate bounds for each strategy and the sum capacity (achieved by DPC) gain over TDMA and BF are given, correspondingly. Finally, numerical results are presented to prove our discussions. With these results, we can easily obtain how far away TDMA and BF are from being optimal in terms of sum rate, and direct the practical system design.
36049595
The Implementation of a Fully Integrated Scheme of self-Configuration and self-Organization (FISCO) on Imote2
Wireless Sensor networks are gaining a role of importance in the research community. In this paper, we choose Imote2 to establish our testbed according to the analysis and the comparison of the hardware capabilities and software characteristics of the current WSNs products. Moreover, we present our hardware, software platforms and "30 motes" testbed to validate and evaluate our Fully Integrated Scheme of self-Configuration and self-Organization (FISCO) while further propose the rudiment of our general routing and data dissemination testbed. In the test stage, experimental results show that each mote in the network obtains the corresponding role and takes correct actions to handle every receiving message.