基于自注意力对抗的深度子空间聚类

子空间聚类(Subspace clustering)是一种当前较为流行的基于谱聚类的高维数据聚类框架.近年来,由于深度神经网络能够有效地挖掘出数据深层特征,其研究倍受各国学者的关注.深度子空间聚类旨在通过深度网络学习原始数据的低维特征表示,计算出数据集的相似度矩阵,然后利用谱聚类获得数据的最终聚类结果.然而,现实数据存在维度过高、数据结构复杂等问题,如何获得更鲁棒的数据表示,改善聚类性能,仍是一个挑战.因此,本文提出基于自注意力对抗的深度子空间聚类算法(SAADSC).利用自注意力对抗网络在自动编码器的特征学习中施加一个先验分布约束,引导所学习的特征表示更具有鲁棒性,从而提高聚类精度.通过在多个数据集上的实验,结果表明本文算法在精确率(ACC)、标准互信息(NMI)等指标上都优于目前最好的方法.     

基于自注意力对抗的深度子空间聚类

子空间聚类(Subspace clustering)是一种当前较为流行的基于谱聚类的高维数据聚类框架.近年来,由于深度神经网络能够有效地挖掘出数据深层特征,其研究倍受各国学者的关注.深度子空间聚类旨在通过深度网络学习原始数据的低维特征表示,计算出数据集的相似度矩阵,然后利用谱聚类获得数据的最终聚类结果.然而,现实数据存在维度过高、数据结构复杂等问题,如何获得更鲁棒的数据表示,改善聚类性能,仍是一个挑战.因此,本文提出基于自注意力对抗的深度子空间聚类算法(SAADSC).利用自注意力对抗网络在自动编码器的特征学习中施加一个先验分布约束,引导所学习的特征表示更具有鲁棒性,从而提高聚类精度.通过在多个数据集上的实验,结果表明本文算法在精确率(ACC)、标准互信息(NMI)等指标上都优于目前最好的方法.     

基于自注意力对抗的深度子空间聚类

子空间聚类(Subspace clustering)是一种当前较为流行的基于谱聚类的高维数据聚类框架.近年来,由于深度神经网络能够有效地挖掘出数据深层特征,其研究倍受各国学者的关注.深度子空间聚类旨在通过深度网络学习原始数据的低维特征表示,计算出数据集的相似度矩阵,然后利用谱聚类获得数据的最终聚类结果.然而,现实数据存在维度过高、数据结构复杂等问题,如何获得更鲁棒的数据表示,改善聚类性能,仍是一个挑战.因此,本文提出基于自注意力对抗的深度子空间聚类算法(SAADSC).利用自注意力对抗网络在自动编码器的特征学习中施加一个先验分布约束,引导所学习的特征表示更具有鲁棒性,从而提高聚类精度.通过在多个数据集上的实验,结果表明本文算法在精确率(ACC)、标准互信息(NMI)等指标上都优于目前最好的方法.     

结合降噪和自注意力的深度聚类算法

近几年,联合聚类划分和表示学习的深度聚类方法提供了出色的聚类性能,但随着图像质量的下降(比如噪声图像),聚类结果还不能令人满意.为此,提出一种新的深度聚类算法(DDC).深度卷积降噪自编码器学习噪声数据的特征表示;自注意力机制提高网络捕获局部关键信息的能力;端到端的联合训练得到合适的特征表示并完成聚类分配;对数据点和类中心的相似度赋予不同的权重,扩大同类和异类之间的差异.在公开图像数据集上的实验表明DDC算法的聚类性能更高;并与其他深度聚类算法进行对比,例如在COIL-20上DDC的聚类精度是0.803,而DEC算法仅是0.597.总之,结合自注意力和深度卷积降噪自编码器的DDC算法能对噪声图像进行更有效的聚类分析,扩大了图像聚类的应用范围.     

一种基于深度谱空网络的高光谱图像聚类方法

通常无监督算法在对高光谱数据进行聚类时仅使用光谱信息,忽略了空间信息,使得聚类准确率较低.针对上述问题提出一种基于深度谱空网络和无监督判别极限学习的高光谱图像聚类算法.利用深度谱空网络对高光谱数据进行光谱特征和空间特征的分层交叉学习,通过反复学习获得深度空谱特征,为后续无监督聚类提供方便.在三种高光谱图像上进行实验,结果表明,该算法获得的聚类效果优于其他基于极限学习机的方法和其他无监督方法.     

基于深度自编码的多视图子空间聚类网络

传统子空间浅层聚类模型对于多视图和非线性数据的聚类性能不佳。为此,提出一种基于深度自编码器的多视图子空间聚类网络模型,通过在深度自编码器中引入子空间聚类中的“自我表示”特性以及加权稀疏表示,提升了多视图子空间聚类算法的学习能力。推导的深度自编码多视图子空间聚类算法能够聚类具有复杂结构的数据点。通过多视图数据集验证了提出算法的有效性。结果表明,该方法能够有效地挖掘数据固有的多样性聚类结构,并利用多个视图之间互补信息,在性能上与现有方法相比有较大的提升。     

一种基于ELM-AE特征表示的谱聚类算法

在实际应用中,数据点中包含的冗余特征和异常值(噪声)严重影响了聚类中更显著的特征的发现,大大降低了聚类性能。本文提出了一种基于ELM-AE (extreme learning machine as autoencoder)特征表示的谱聚类算法(spectral clustering via extreme learning machine as autoencoder, SC-ELM-AE)。ELM-AE通过奇异值分解学习源数据主要特征表示,使用输出权值实现从特征空间到原输入数据的重构;再将该特征表示空间作为输入进行谱聚类。实验表明,在5个UCI数据集验证中,SC-ELM-AE算法性能优于传统的K-Means、谱聚类等现有算法,特别是在复杂高维数据集PEMS-SF和TDT2_10上,聚类平均精确度均提高30%以上。     

引入低秩约束先验的深度子空间聚类

大多数子空间聚类算法将高维数据映射到低维子空间时不能较好捕获数据间几何结构.针对上述问题,文中提出引入低秩约束先验的深度子空间聚类算法,兼顾数据全局和局部结构信息.算法结合低秩表示与深度自编码器,利用低秩约束捕获数据全局结构,并将约束神经网络的潜在特征表示为低秩.自编码通过最小化重构误差进行非线性低维子空间映射,保留数据的局部特性.以多元逻辑回归函数作为判别模型,预测子空间分割.整个算法在无监督联合学习框架下进行优化.在5个数据集上的实验验证文中方法的有效性.     

快速可扩展的子空间聚类算法*

子空间聚类算法只能处理小规模数据,且无法处理样本外数据.针对此问题,文中提出采用二次采样策略的子空间聚类框架(TSSC).该框架由两个核心部件组成:判别性协作表示(DCR)与多尺度K近邻(KNN)采样方法.在TSSC中,DCR首先结合多尺度KNN对数据点进行特征变换,从而保证属于同一子空间的点有更一致的表示.为了提高算法的可扩展性,TSSC在新的特征空间中使用多尺度KNN对数据进行二次采样,并根据采样点获得的初步聚类结果训练线性分类器,最后根据学习得到的分类器对剩余样本点进行分类,获得最终的聚类结果.在真实数据集上的实验验证TSSC的有效性.     

基于图信息的自监督多视角子空间聚类

多视角子空间聚类方法通常用于处理高维度、复杂结构的数据.现有的大多数多视角子空间聚类方法通过挖掘潜在图信息进行数据分析与处理,但缺乏对潜在子空间表示的监督过程.针对这一问题,本文提出一种新的多视角子空间聚类方法,即基于图信息的自监督多视角子空间聚类(SMSC).它将谱聚类与子空间表示相结合形成统一的深度学习框架.SMS...     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

煤矿井下安全监控分站的设计及其仿真实现

为了设计一种具有低成本、低功耗、易操作、功能强且可靠性高的煤矿井下安全分站,针对煤矿安全生产实际,文章提出了采用MCS-51系列单片机为核心、具有CAN总线通信接口的煤矿井下安全监控分站的设计方案;首先给出煤矿井下安全监控分站的整体构架设计,然后着重阐述模拟量输入信号处理系统的设计过程,最后说明单片机最小系统及其键盘、显示、报警、通信等各个组成部分的设计;为验证设计方案的可行性与有效性,使用Proteus软件对设计内容进行仿真验证,设计的煤矿井下安全监控分站具有瓦斯、温度等模拟量参数超标报警功能和电机开停、风门开闭等开关量指示功能;仿真结果表明:设计的煤矿井下安全监控分站具有一定的实际应用价值.     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

View-based model-driven architecture for enhancing maintainability of data access services

In modern service-oriented architectures, database access is done by a special type of services, the so-called data access services (DAS). Though, particularly in data-intensive applications, using and developing DAS are very common today, the link between the DAS and their implementation, e.g. a layer of data access objects (DAOs) encapsulating the database queries, still is not sufficiently elaborated, yet. As a result, as the number of DAS grows, finding the desired DAS for reuse and/or associated documentation can become an impossible task. In this paper we focus on bridging this gap between the DAS and their implementation by presenting a view-based, model-driven data access architecture (VMDA) managing models of the DAS, DAOs and database queries in a queryable manner. Our models support tailored views of different stakeholders and are scalable with all types of DAS implementations. In this paper we show that our view-based and model driven architecture approach can enhance software development productivity and maintainability by improving DAS documentation. Moreover, our VMDA opens a wide range of applications such as evaluating DAS usage for DAS performance optimization. Furthermore, we provide tool support and illustrate the applicability of our VMDA in a large-scale case study. Finally, we quantitatively prove that our approach performs with acceptable response times.     

A pragmatic multi-method investigation of discrepant technological events: Coping,attributions, and ‘accidental’ learning

Discrepant technological events or situations that entail a problem, a misunderstanding or a difficulty with the Information Technology (IT) being employed, are common in the workplace, and can lead to frustration and avoidance behaviors. Little is known, however, about how individuals cope with these events. This paper examines these events by using a multi-method pragmatic approach informed by coping theory. The results of two studies – a critical incident study and an experiment – serve to build and test, respectively, a theoretical model that posits that individuals use a variety of strategies when dealing with these events: they experience negative emotions, make external attributions, and adopt engagement coping strategies directed at solving the event, eventually switching to a disengagement coping strategy when they feel they have no control over the situation. Furthermore, users’ efforts may result in ‘accidental’ learning as they try to overcome the discrepant IT events through engagement coping. The paper ends with a discussion of the results in light of existing literature, future opportunities for research, and implications for practice.