Computing committors in collective variables via Mahalanobis diffusion maps

Activity Recognition Via Classification Constrained Diffusion Maps

Clustering Mixed Data via Diffusion Maps

Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, customer segmentation, trend analysis, pattern recognition and image analysis. Although many clustering algorithms have been proposed most of them deal with clustering of numerical data. Finding the similarity between numeric objects usually relies on a com...

Feature Extraction via Compressed Local Mahalanobis Metrics

We propose a new energy-based ([1]) online learning approach to the problem of extracting small features from high dimensional, noisy datasets. We demonstrate the power of this new technique by applying it to the Detection of cellular nuclei in large hyperspectral images. Thus, we prove that only a minimum number of random, wideband measurements is necessary to achieve efficient feature extract...

Learning semantic features for action recognition via diffusion maps

Efficient modeling of actions is critical for recognizing human actions. Recently, bag of video words (BoVW) representation, in which features computed around spatiotemporal interest points are quantized into video words based on their appearance similarity, has been widely and successfully explored. The performance of this representation however, is highly sensitive to two main factors: the gr...

Computing Science Collective Wisdom

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