User authentication based on foot motion

In nearly all current systems, user authentication mechanism is one time and static. Although such type of user authentication is sufficient for many types of applications, in some scenarios, continuous or periodic re-verification of the identity is desirable, especially in high-security application. In this paper, we study user authentication based on 3D foot motion, which can be suitable for periodic identity re-verification purposes. Three-directional (3D) motion of the foot (in terms of acceleration signals) is collected using a wearable accelerometer sensor attached to the ankle of the person. Ankle accelerations from three directions (up-down, forward-backward and sideways) are analyzed for person authentication. Applied recognition method is based on detecting individual cycles in the signal and then finding best matching cycle pair between two acceleration signals. Using experimental data from 30 subjects, obtained EERs (Equal Error Rates) were in the range of 1.6–23.7% depending on motion directions and shoe types. Furthermore, by combining acceleration signals from 2D and 3D and then applying fusing techniques, recognition accuracies could be improved even further. The achieved performance improvements (in terms of EER) were up to 68.8%.     

松脱件撞击波形检测算法

文中针对核反应设施松动件的检测，以碰撞波形检测为目标，提出了一种基于机器学习的松动件撞击波形检测方法。该方法以核反应设施中的传感器数据为输入基础，利用数据滤波、特征降维、机器学习等组合算法，实现松动件振动波形识别。实验结果表明，对数据进行滤波、降维后，利用GBDT算法，能有效识别不同类型的振动波形。此外，通过t-SNE算法对样本进行可视化后，发现经滤波和特征降维后的数据具有很好的聚类特征，有助于分类算法进一步完成分类识别。     

Learning and matching of dynamic shape manifolds for human action recognition.

In this paper, we learn explicit representations for dynamic shape manifolds of moving humans for the task of action recognition. We exploit locality preserving projections (LPP) for dimensionality reduction, leading to a low-dimensional embedding of human movements. Given a sequence of moving silhouettes associated to an action video, by LPP, we project them into a low-dimensional space to characterize the spatiotemporal property of the action, as well as to preserve much of the geometric structure. To match the embedded action trajectories, the median Hausdorff distance or normalized spatiotemporal correlation is used for similarity measures. Action classification is then achieved in a nearest-neighbor framework. To evaluate the proposed method, extensive experiments have been carried out on a recent dataset including ten actions performed by nine different subjects. The experimental results show that the proposed method is able to not only recognize human actions effectively, but also considerably tolerate some challenging conditions, e.g., partial occlusion, low-quality videos, changes in viewpoints, scales, and clothes; within-class variations caused by different subjects with different physical build; styles of motion; etc.     

基于低秩张量分析的高光谱图像降维与分类

提出一种用于高光谱图像降维和分类的分块低秩张量分析方法。该算法以提高分类精度为目标,对图像张量分块进行降维和分类。将高光谱图像分成若干子张量,不仅保存了高光谱图像的三维数据结构,利用了空间与光谱维度的关联性,还充分挖掘了图像局部的空间相关性。与现有的张量分析法相比,这种分块处理方法克服了图像的整体空间相关性较弱以及子空间维度的设定对降维效果的负面影响。只要子空间维度小于子张量维度,所提议的分块算法就能取得较好的降维效果,其分类精度远远高于不分块的算法,从而无需借助原本就不可靠的子空间维度估计法。仿真和真实数据的实验结果表明,所提议分块低秩张量分析算法明显地表现出较好的降维效果,具有较高的分类精度。     

A novel dimensionality reduction technique based on kernel optimization through graph embedding

In this paper, we propose a new method for kernel optimization in kernel-based dimensionality reduction techniques such as kernel principal component analysis and kernel discriminant analysis. The main idea is to use the graph embedding framework for these techniques and, therefore, by formulating a new minimization problem to simultaneously optimize the kernel parameters and the projection vectors of the chosen dimensionality reduction method. Experimental results are conducted in various datasets, varying from real-world publicly available databases for classification benchmarking to facial expressions and face recognition databases. Our proposed method outperforms other competing ones in classification performance. Moreover, our method provides a systematic way to deal with kernel parameters whose calculation was treated rather superficially so far and/or experimentally, in most of the cases.     

Exploiting Motion Correlations in 3-D Articulated Human Motion Tracking

In 3-D articulated human motion tracking, the curse of dimensionality renders commonly-used particle-filter-based approaches inefficient. Also, noisy image measurements and imperfect feature extraction call for strong motion prior. We propose to learn the correlation between the right-side and the left-side human motion using partial least square (PLS) regression. The correlation effectively constrains the sampling of the proposal distribution to portions of the parameter space that correspond to plausible human motions. The learned correlation is then used as motion prior in designing a Rao–Blackwellized particle filter algorithm, RBPF-PLS, which estimates only one group of state variables using the Monte Carlo method, leaving the other group being exactly computed through an analytical filter that utilizes the learned motion correlation. We quantitatively assessed the accuracy of the proposed algorithm with challenging HumanEva-I/II data set. Experiments with comparison with both the annealed particle filter and the standard particle filter show that the proposed method achieves lower estimation error in processing challenging real-world data of 3-D human motion. In particular, the experiments demonstrate that the learned motion correlation model generalizes well to motions outside of the training set and is insensitive to the choice of the training subjects, suggesting the potential wide applicability of the method.      

最大边缘准则图形嵌入在人脸识别中的应用

局部线性嵌入是一种有效地非线性维数约减方法,它能保持降维后的数据与原空间有相同的拓扑关系。但是这种方法在降维处理、可视化以及数据分类方面应用不是很广泛,针对上述问题,提出了一种新的、有效的降维以及数据分类方法——基于最大边缘准则图形嵌入方法。该方法首先构建最近邻关系图聚合数据点之间的最近邻样本,同时最大化类间间隔,保证不同类之间数据可分性大,从而更好地实现数据分类。最后,该方法的有效性分别在ORL及Yale两大人脸库上得到了验证。     

Hyperspectral data analysis and supervised feature reduction viaprojection pursuit

As the number of spectral bands of high-spectral resolution data increases, the ability to detect more detailed classes should also increase, and the classification accuracy should increase as well. Often the number of labelled samples used for supervised classification techniques is limited, thus limiting the precision with which class characteristics can be estimated. As the number of spectral bands becomes large, the limitation on performance imposed by the limited number of training samples can become severe. A number of techniques for case-specific feature extraction have been developed to reduce dimensionality without loss of class separability. Most of these techniques require the estimation of statistics at full dimensionality in order to extract relevant features for classification. If the number of training samples is not adequately large, the estimation of parameters in high-dimensional data will not be accurate enough. As a result, the estimated features may not be as effective as they could be. This suggests the need for reducing the dimensionality via a preprocessing method that takes into consideration high-dimensional feature-space properties. Such reduction should enable the estimation of feature-extraction parameters to be more accurate. Using a technique referred to as projection pursuit (PP), such an algorithm has been developed. This technique is able to bypass many of the problems of the limitation of small numbers of training samples by making the computations in a lower-dimensional space, and optimizing a function called the projection index. A current limitation of this method is that, as the number of dimensions increases, it is likely that a local maximum of the projection index will be found that does not enable one to fully exploit hyperspectral-data capabilities     

Generative-discriminative basis learning for medical imaging

This paper presents a novel dimensionality reduction method for classification in medical imaging. The goal is to transform very high-dimensional input (typically, millions of voxels) to a low-dimensional representation (small number of constructed features) that preserves discriminative signal and is clinically interpretable. We formulate the task as a constrained optimization problem that combines generative and discriminative objectives and show how to extend it to the semi-supervised learning (SSL) setting. We propose a novel large-scale algorithm to solve the resulting optimization problem. In the fully supervised case, we demonstrate accuracy rates that are better than or comparable to state-of-the-art algorithms on several datasets while producing a representation of the group difference that is consistent with prior clinical reports. Effectiveness of the proposed algorithm for SSL is evaluated with both benchmark and medical imaging datasets. In the benchmark datasets, the results are better than or comparable to the state-of-the-art methods for SSL. For evaluation of the SSL setting in medical datasets, we use images of subjects with mild cognitive impairment (MCI), which is believed to be a precursor to Alzheimer's disease (AD), as unlabeled data. AD subjects and normal control (NC) subjects are used as labeled data, and we try to predict conversion from MCI to AD on follow-up. The semi-supervised extension of this method not only improves the generalization accuracy for the labeled data (AD/NC) slightly but is also able to predict subjects which are likely to converge to AD.     

一种基于图的线性判别分析方法

线性判别分析(LDA)作为全局性降维的方法,在处理局部性边缘点的问题上存在不足,可能会导致边缘点的误分。针对该问题,提出一种新的降维方法,该方法基于图学习的思想,重新构造图,使得同类之间向中心靠拢的同时,不同类的K个近邻点远离该类中心。这样,高维数据在嵌入低维的过程中保持了样本的局部边缘点的特性,从而保证了边缘点的正确分类。通过在UCI数据集和人脸数据库中实验,结果表明本方法的有效性。     

Classification of neck movement patterns related to whiplash-associated disorders using neural networks

This paper presents a new method for classification of neck movement patterns related to Whiplash-associated disorders (WAD) using a resilient backpropagation neural network (BPNN). WAD are a common diagnosis after neck trauma, typically caused by rear-end car accidents. Since physical injuries seldom are found with present imaging techniques, the diagnosis can be difficult to make. The active range of the neck is often visually inspected in patients with neck pain, but this is a subjective measure, and a more objective decision support system, that gives a reliable and more detailed analysis of neck movement pattern, is needed. The objective of this study was to evaluate the predictive ability of a BPNN, using neck movement variables as input. Three-dimensional (3-D) neck movement data from 59 subjects with WAD and 56 control subjects were collected with a ProReflex system. Rotation angle and angle velocity were calculated using the instantaneous helical axis method and motion variables were extracted. A principal component analysis was performed in order to reduce data and improve the BPNN performance. BPNNs with six hidden nodes had a predictivity of 0.89, a sensitivity of 0.90 and a specificity of 0.88, which are very promising results. This shows that neck movement analysis combined with a neural network could build the basis of a decision support system for classifying suspected WAD, even though further evaluation of the method is needed.     

基于多特征多分辨率融合的高光谱图像分类

由于数据维数高,利用高光谱数据对地物进行分类,常规方法难以获得令人满意的结果,在基于小波多分辨率融合方法进行特征图像的提取过程中,提出了利用多个空间特征所构成的特征矢量确定多分辨率融合权值的算法,有效地降低了原始图像的数据维并获得了用于后续分类的特征图像．对AVIRIS数据进行的实验表明,利用新方法提取的特征进行分类,获得了高于传统方法确定融合权值的结果。     

Improving reliability of gene selection from microarray functional genomics data

Constructing a classifier based on microarray gene expression data has recently emerged as an important problem for cancer classification. Recent results have suggested the feasibility of constructing such a classifier with reasonable predictive accuracy under the circumstance where only a small number of cancer tissue samples of known type are available. Difficulty arises from the fact that each sample contains the expression data of a vast number of genes and these genes may interact with one another. Selection of a small number of critical genes is fundamental to correctly analyze the otherwise overwhelming data. It is essential to use a multivariate approach for capturing the correlated structure in the data. However, the curse of dimensionality leads to the concern about the reliability of selected genes. Here, we present a new gene selection method in which error and repeatability of selected genes are assessed within the context of M-fold cross-validation. In particular, we show that the method is able to identify source variables underlying data generation.     

RFID-based 3D human pose tracking: A subject generalization approach

Three-dimensional (3D) human pose tracking has recently attracted more and more attention in the computer vision field. Real-time pose tracking is highly useful in various domains such as video surveillance, somatosensory games, and human-computer interaction. However, vision-based pose tracking techniques usually raise privacy concerns, making human pose tracking without vision data usage an important problem. Thus, we propose using Radio Frequency Identification (RFID) as a pose tracking technique via a low-cost wearable sensing device. Although our prior work illustrated how deep learning could transfer RFID data into real-time human poses, generalization for different subjects remains challenging. This paper proposes a subject-adaptive technique to address this generalization problem. In the proposed system, termed Cycle-Pose, we leverage a cross-skeleton learning structure to improve the adaptability of the deep learning model to different human skeletons. Moreover, our novel cycle kinematic network is proposed for unpaired RFID and labeled pose data from different subjects. The Cycle-Pose system is implemented and evaluated by comparing its prototype with a traditional RFID pose tracking system. The experimental results demonstrate that Cycle-Pose can achieve lower estimation error and better subject generalization than the traditional system.     

Multilinear discriminant analysis for face recognition.

There is a growing interest in subspace learning techniques for face recognition; however, the excessive dimension of the data space often brings the algorithms into the curse of dimensionality dilemma. In this paper, we present a novel approach to solve the supervised dimensionality reduction problem by encoding an image object as a general tensor of second or even higher order. First, we propose a discriminant tensor criterion, whereby multiple interrelated lower dimensional discriminative subspaces are derived for feature extraction. Then, a novel approach, called k-mode optimization, is presented to iteratively learn these subspaces by unfolding the tensor along different tensor directions. We call this algorithm multilinear discriminant analysis (MDA), which has the following characteristics: 1) multiple interrelated subspaces can collaborate to discriminate different classes, 2) for classification problems involving higher order tensors, the MDA algorithm can avoid the curse of dimensionality dilemma and alleviate the small sample size problem, and 3) the computational cost in the learning stage is reduced to a large extent owing to the reduced data dimensions in k-mode optimization. We provide extensive experiments on ORL, CMU PIE, and FERET databases by encoding face images as second- or third-order tensors to demonstrate that the proposed MDA algorithm based on higher order tensors has the potential to outperform the traditional vector-based subspace learning algorithms, especially in the cases with small sample sizes.     

面向高光谱遥感影像分类的监督多流形鉴别嵌入方法

流形学习方法可以发现嵌入于高维观测数据中的低维流形结构,但是传统的流形学习算法都是假设所有数据位于单一流形上,忽略了高维数据中不同的子集可能存在不同的流形.针对上述问题,本文提出一种监督多流形鉴别嵌入的维数约简方法,并应用于高光谱遥感影像分类.该方法首先利用样本数据的类别标签进行多子流形划分,在此基础上采用图嵌入理论构造流形内图和流形间图,然后通过最小化流形内距离同时最大化流形间距离以增强类内数据聚集性和类间数据分散性,提取低维鉴别特征,改善地物分类性能.在University of Pavia （PaviaU）和Kennedy Space Center （KSC）高光谱数据集上的实验表明,相较于其他单流形算法和多流形算法,该方法取得了更高的分类精度,在随机选取2%训练样本时,其总体分类精度分别达到88.04%和84.53%,有效提升了地物分类性能.     

Estimating effective degrees of freedom in motor systems.

Studies of the degrees of freedom and "synergies" in musculoskeletal systems rely critically on algorithms to estimate the "dimension" of kinematic or neural data. Linear algorithms such as principal component analysis (PCA) are the most popular. However, many biological data (or realistic experimental data) may be better represented by nonlinear sets than linear subspaces. We evaluate the performance of PCA and compare it to two nonlinear algorithms [Isomap and our novel pointwise dimension estimation (PD-E)] using synthetic and motion capture data from a robotic arm with known kinematic dimensions, as well as motion capture data from human hands. We find that PCA can lead to more accurate dimension estimates when considering additional properties of the PCA residuals, instead of the dominant method of using a threshold of variance captured. In contrast to the single integer dimension estimates of PCA and Isomap, PD-E provides a distribution and range of estimates of fractal dimension that identify the heterogeneous geometric structure in the experimental data. A strength of the PD-E method is that it associates a distribution of dimensions to the data. Since there is no a priori reason to assume that the sets of interest have a single dimension, these distributions incorporate more information than a single summary statistic. Our preliminary findings suggest that fewer than ten DOFs are involved in some hand motion tasks. Contrary to common opinion regarding fractal dimension methods, PD-E yielded reasonable results with reasonable amounts of data. Given the complex nature of experimental and biological data, we conclude that it is necessary and feasible to complement PCA with methods that take into consideration the nonlinear properties of biological systems for a more robust estimation of their DOFs.     

结合K-L散度和互信息的无监督波段选择算法

波段选择是重要的高光谱图像降维手段。为了达到降维的目的,提出结合K-L散度和互信息的无监督波段选择算法,并进行了理论分析和实验验证。首先选出信息熵最大的波段作为初始波段,然后将散度与互信息量的比值定义为联合散度互信息（KLMI）准则,选择KLMI值大且信息量也大的波段加入波段子集中,选出信息量大且相似度低的波段集合,最终利用k最近邻分类算法实现了基于最大方差主成分分析算法、聚类算法、互信息算法和本文中方法的真实高光谱数据分类实验。结果表明,本文中的算法总体分类精度和κ系数均达到0.8以上,高于其它算法;大多数地物的分类精度均得到提升,具有较好的分类性能。该算法是一种实用的高光谱图像降维算法。     

Combating highly imbalanced steganalysis with small training samples using feature selection,

We consider a particular paradigm of steganalysis, namely, highly imbalanced steganalysis with small training samples, in which the cover images always significantly outnumber the stego ones. Researchers have rigorously studied sampling and learning algorithms as well as feature selection approaches to the class imbalance problem, but the research in the steganalysis domain is rare. This study provides a systematic comparison of eight feature selection metrics and of three types of methods developed for the imbalanced data classification problem in the steganalysis domain. Each metric is compared across three different classifiers and four steganalytic features. The efficiency of the metrics is evaluated to determine which performs best with minimal features selected. The performance of the three types of methods and their combinations is examined. Moreover, we also investigate the effect of feature dimensionality, sample number and imbalance degree on the performance of feature selection inresolving imbalanced image steganalysis.