15,681 research outputs found
Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification
Multiple kernel learning (MKL) method is generally believed to perform better
than single kernel method. However, some empirical studies show that this is
not always true: the combination of multiple kernels may even yield an even
worse performance than using a single kernel. There are two possible reasons
for the failure: (i) most existing MKL methods assume that the optimal kernel
is a linear combination of base kernels, which may not hold true; and (ii) some
kernel weights are inappropriately assigned due to noises and carelessly
designed algorithms. In this paper, we propose a novel MKL framework by
following two intuitive assumptions: (i) each kernel is a perturbation of the
consensus kernel; and (ii) the kernel that is close to the consensus kernel
should be assigned a large weight. Impressively, the proposed method can
automatically assign an appropriate weight to each kernel without introducing
additional parameters, as existing methods do. The proposed framework is
integrated into a unified framework for graph-based clustering and
semi-supervised classification. We have conducted experiments on multiple
benchmark datasets and our empirical results verify the superiority of the
proposed framework.Comment: Accepted by IJCAI 2018, Code is availabl
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State-of-the-art on research and applications of machine learning in the building life cycle
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science
Focused Proofreading: Efficiently Extracting Connectomes from Segmented EM Images
Identifying complex neural circuitry from electron microscopic (EM) images
may help unlock the mysteries of the brain. However, identifying this circuitry
requires time-consuming, manual tracing (proofreading) due to the size and
intricacy of these image datasets, thus limiting state-of-the-art analysis to
very small brain regions. Potential avenues to improve scalability include
automatic image segmentation and crowd sourcing, but current efforts have had
limited success. In this paper, we propose a new strategy, focused
proofreading, that works with automatic segmentation and aims to limit
proofreading to the regions of a dataset that are most impactful to the
resulting circuit. We then introduce a novel workflow, which exploits
biological information such as synapses, and apply it to a large dataset in the
fly optic lobe. With our techniques, we achieve significant tracing speedups of
3-5x without sacrificing the quality of the resulting circuit. Furthermore, our
methodology makes the task of proofreading much more accessible and hence
potentially enhances the effectiveness of crowd sourcing
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