Non-Negative Local Sparse Coding for Subspace Clustering

Subspace sparse coding (SSC) algorithms have proven to be beneficial to clustering problems. They provide an alternative data representation in which the underlying structure of the clusters can be better captured. However, most of the research in this area is mainly focused on enhancing the sparse coding part of the problem. In contrast, we introduce a novel objective term in our proposed SSC framework which focuses on the separability of data points in the coding space. We also provide mathematical insights into how this local-separability term improves the clustering result of the SSC framework. Our proposed non-linear local SSC algorithm (NLSSC) also benefits from the efficient choice of its sparsity terms and constraints. The NLSSC algorithm is also formulated in the kernel-based framework (NLKSSC) which can represent the nonlinear structure of data. In addition, we address the possibility of having redundancies in sparse coding results and its negative effect on graph-based clustering problems. We introduce the link-restore post-processing step to improve the representation graph of non-negative SSC algorithms such as ours. Empirical evaluations on well-known clustering benchmarks show that our proposed NLSSC framework results in better clusterings compared to the state-of-the-art baselines and demonstrate the effectiveness of the link-restore post-processing in improving the clustering accuracy via correcting the broken links of the representation graph.Comment: 15 pages, IDA 2018 conferenc

Long-term outcomes of early childhood science education: insight from a cross-national comparative case study on conceptual understanding of science

The purpose of this research was to explore the long term outcomes of either participating or not participating in early childhood science education on Grade 6 students’ conceptual understanding of science. The research is situated in a conceptual framework that evokes Piagetian developmental levels as both potential curriculum constraints and potential models of efficacy. The research design was a multiple case study of Grade 6 children from three schools in China (n=140) who started formal science education in the third grade, and Grade 6 children from three matched schools in Australia (n=105) who started learning science in kindergarten. The students’ understanding was assessed by a science quiz and in-depth interview. The data showed that participating children from the high socio-economic schools in China and Australia had similar understandings of science. Divergence between the medium and low socio-economic schools, however, indicated that the grounding in early childhood science education in Australia may have placed these children at an advantage. Alternative explanations for the divergence including the nature of classroom instruction in the two countries are discussed

Brain-Computer Interface Based on Generation of Visual Images

This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects) and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive Bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP) classifier

Dimensions of meta-'conceptual change learning' in science education The role of metacognition in the durability and contextual use of primary pupils' conceptions

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN043156 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Adaptive context-aware filter fusion for face recognition on bad illumination

At present, the performance of face recognition system depends much on the variations in illumination. To solve this problem, this paper presents an adaptable face recognition approach that uses filter fusion representation. The key idea is to use context-aware filter fusion to get better image from a bad illumination one. Genetic algorithm is the tool for adaptation for individual context category. These can provide robust face recognition on illumination context-awareness under uneven environments. Gabor wavelet representation can also provide a robust feature for image enhancement. Using these approaches, we have developed a robust face recognition technique that can recognize with a notable success and it has been tested on Inha DB and FERET face image