Pedestrian detection is a problem of considerable practical interest. Adding to the list of
successful applications of deep learning methods to vision, we report state-of-theart and …

In many recent object recognition systems, feature extraction stages are generally
composed of a filter bank, a non-linear transformation, and some sort of feature pooling …

A full-rank matrix \bfA∈R^n*m with n<m generates an underdetermined system of linear
equations \bfAx=\bfb having infinitely many solutions. Suppose we seek the sparsest …

We propose an unsupervised method for learning multi-stage hierarchies of sparse
convolutional features. While sparse coding has become an increasingly popular method for …

We describe Nonnegative Double Singular Value Decomposition (NNDSVD), a new method
designed to enhance the initialization stage of nonnegative matrix factorization (NMF) …

Smart meters play vital roles in the aspects of the management and operation of smart grids
such as demand response, energy efficiency improvement, and electricity pricing. Massive …

We develop a new model and algorithms for machine learning-based learning analytics,
which estimate a learner's knowledge of the concepts underlying a domain, and content …

Although nonnegative matrix factorization (NMF) favors a sparse and part-based
representation of nonnegative data, there is no guarantee for this behavior. Several authors …

We develop a view-normalization approach to multi-view face and gait recognition. An
image-based visual hull (IBVH) is computed from a set of monocular views and used to …

The spectral features in hyperspectral imagery (HSI) contain significant structure that, if
properly characterized, could enable more efficient data acquisition and improved data …