Blind source separation by fully nonnegative constrained iterative volume maximization

Blind source separation (BSS) has been widely discussed in many real applications. Recently, under the assumption that both of the sources and the mixing matrix are nonnegative, Wang develop an amazing BSS method by using volume maximization. However, the algorithm that they have proposed can guarantee the nonnegativities of the sources only, but cannot obtain a nonnegative mixing matrix necessarily. In this letter, by introducing additional constraints, a method for fully nonnegative constrained iterative volume maximization (FNCIVM) is proposed. The result is with more interpretation, while the algorithm is based on solving a single linear programming problem. Numerical experiments with synthetic signals and real-world images are performed, which show the effectiveness of the proposed method.

Maximum contrast analysis for nonnegative blind source separation

In this paper, we propose a maximum contrast analysis (MCA) method for nonnegative blind source separation, where both the mixing matrix and the source signals are nonnegative. We first show that the contrast degree of the source signals is greater than that of the mixed signals. Motivated by this observation, we propose an MCA-based cost function. It is further shown that the separation matrix can be obtained by maximizing the proposed cost function. Then we derive an iterative determinant maximization algorithm for estimating the separation matrix. In the case of two sources, a closed-form solution exists and is derived. Unlike most existing blind source separation methods, the proposed MCA method needs neither the independence assumption, nor the sparseness requirement of the sources. The effectiveness of the new method is illustrated by experiments using X-ray images, remote sensing images, infrared spectral images, and real-world fluorescence microscopy images.

Nonnegative blind source separation by sparse component analysis based on determinant measure

The problem of nonnegative blind source separation (NBSS) is addressed in this paper, where both the sources and the mixing matrix are nonnegative. Because many real-world signals are sparse, we deal with NBSS by sparse component analysis. First, a determinant-based sparseness measure, named D-measure, is introduced to gauge the temporal and spatial sparseness of signals. Based on this measure, a new NBSS model is derived, and an iterative sparseness maximization (ISM) approach is proposed to solve this model. In the ISM approach, the NBSS problem can be cast into row-to-row optimizations with respect to the unmixing matrix, and then the quadratic programming (QP) technique is used to optimize each row. Furthermore, we analyze the source identifiability and the computational complexity of the proposed ISM-QP method. The new method requires relatively weak conditions on the sources and the mixing matrix, has high computational efficiency, and is easy to implement. Simulation results demonstrate the effectiveness of our method.

Nonnegative Blind Source Separation by Sparse Component Analysis Based on Determinant Measure

The problem of nonnegative blind source separation (NBSS) is addressed in this paper, where both the sources and the mixing matrix are nonnegative. Because many real-world signals are sparse, we deal with NBSS by sparse component analysis. First, a determinant-based sparseness measure, named D-measure, is introduced to gauge the temporal and spatial sparseness of signals. Based on this measure, a new NBSS model is derived, and an iterative sparseness maximization (ISM) approach is proposed to solve this model. In the ISM approach, the NBSS problem can be cast into row-to-row optimizations with respect to the unmixing matrix, and then the quadratic programming (QP) technique is used to optimize each row. Furthermore, we analyze the source identifiability and the computational complexity of the proposed ISM-QP method. The new method requires relatively weak conditions on the sources and the mixing matrix, has high computational efficiency, and is easy to implement. Simulation results demonstrate the effectiveness of our method.

Developing wikis to foster web-based learning communities : an iterative approach

This paper discusses ongoing research at Deakin University, which focuses on developing wikis to foster web-based learning communities. Research to date has used wikis to facilitate collaborative icebreaker exercises, discussions, and to create knowledge repositories. Student feedback has contributed to the iterative revision of the wiki interface, the icebreaker exercise and the development of new tasks for students to complete using the wiki. The analysis and discussion of the experiments presented in this paper focuses on usage trends such as the signature, viewing and editing patterns exhibited by the student cohort. The community building potential of wikis is discussed, highlighting the specific wiki features that can be used to foster a sense of community in a web-based learning environment. Finally, issues surrounding the development of web-based learning communities, which have emerged through the wiki study, are discussed and future directions are outlined.

A maximal frequent itemset approach for web document clustering

To efficiently and yet accurately cluster Web documents is of great interests to Web users and is a key component of the searching accuracy of a Web search engine. To achieve this, this paper introduces a new approach for the clustering of Web documents, which is called maximal frequent itemset (MFI) approach. Iterative clustering algorithms, such as K-means and expectation-maximization (EM), are sensitive to their initial conditions. MFI approach firstly locates the center points of high density clusters precisely. These center points then are used as initial points for the K-means algorithm. Our experimental results tested on 3 Web document sets show that our MFI approach outperforms the other methods we compared in most cases, particularly in the case of large number of categories in Web document sets.

Iterative variable aggregation and disaggregation in IP : an application

This paper, using the Unconstrained Shape Matrix Optimization Problem as a test bed, we investigate various aspects of variable aggregation and disaggregation for a class of integer programs that contains binary expansion. We present theoretical and numerical results, and propose an iterative algorithm for exact solutions.

Education as a determinant of health and well-being

This chapter is concerned with education as a factor in shaping life opportunities.  Education affects, and is affected by, individual and collective health and well-being.  It is now well established that the health and well-being of students impacts on their educational experience and outcomes, and that those experiences and outcomes impact on students' occupational futures, their future health and well-being and their level of participation as citizens.  Policy makers and practitioners are incresingly attentive to the relationship between education and health because of evidence highlighting the cyclical relationship between the economic and social conttext of schools, poor health and the well-being of students, and educational under-acheivement.

Deakin University Library website 2.0 : iterative change for maximum gain

The purpose of library websites is evolving. Deakin University Library originally undertook to redevelop its website to provide improved access to information, resources and services and to better meet clients in their space. The first phase redeveloped the library homepage and top level link pages. During this time, social networking applications were becoming part of higher education. There were new choices: the Library website and search tools could undergo significant metamorphosis; adopt Web2.0 functionality and move from being the public face of the online library to the public space of its online community, with students and staff as active partners in its development.

Exchange as a determinant in corporate citizenship : exploratory action research into the social construction of corporate citizenship

This study attempts to achieve two things. Firstly it contextualizes corporate citizenship drawing on scholarly, government, media, legal and business discourses which when viewed as a whole, reveals the importance of exchange as a central determinant in how all the major themes or subfields of corporate citizenship function and subsequently become valued within public discourse. Secondly, it reports on exploratory action research where I as a researcher occupied a central role in understanding and contributing towards how organizational settings socially construct and evolve corporate citizenship in real time through various exchange behaviour, drawing from four years field research within BP and its interactions with the external world. This research contributes to new knowledge by building a rare contextual understanding into how cultural change evolves over time within an organization, from its public face, through policy, down into employee and stakeholder reactions, including identifying the crucial role played by Cultural bridges’ in shifting entrenched organizational culture towards embracing new, more sustainable ways of doing business, and additionally how practitioners can legitimately act as a researcher in facilitating this process by assisting an organization to move from simple, transactional relationships to more sustainable integrated social, financial and environmental exchange between business and its broader context. Importantly, this research develops entirely new theoretical models for understanding the social application and commercial value of corporate citizenship to both business and society.

An iterative allocation mechanism in the edgeworth box

This paper considers an iterative allocation mechanism which resolves the problem of multiplicity of allocations given by a mechanism in commodity space. At each stage, the average of the extreme allocations is taken and used as the starting point of the next stage. As long as the mechanism is individually rational and Pareto optimal, this iterative procedure yields a unique final allocation which is also individually rational and Pareto optimal. (JEL C63, C71)