This paper derives a novel linear position constraint for cameras seeing a common scene point, which leads to a direct linear method for global camera translation estimation. Unlike previous solutions, this method deals with collinear camera motion and weak image association at the same time. The final linear formulation does not involve the coordinates of scene points, which makes it efficient...

Background: Migraine headache without aura is the most common type of migraine especially among pediatric patients. It has always been a great challenge of migraine diagnosis using quantitative electroencephalography measurements through feature classification. It has been proven that different feature extraction and classification methods vary in terms of performance regarding detection and di...

The ability to train deep architectures has led to many developments in parametric, non-linear dimensionality reduction but with little attention given to algorithms based on convolutional feature extraction without backpropagation training. This paper aims to fill this gap in the context of supervised Mahalanobis metric learning. Modifying two existing approaches to model latent space similari...

Human vision has marvelous ability in extracting linear features from images, such as roads, rivers and so on. In this paper we present a new method to simulate this ability. Our method is based on some general grouping factors arising at two levels. At the first level, grouping factors are identified as direct bar-bar interaction and orientation interaction. Bar-bar interaction is shortranged ...

This paper addresses the issues associated with performing feature or parameter selection for non-linear classifiers using a basis pursuit regularization framework. New results on representing the feature selection problem as a primal/dual calculation for both hard and soft margin classification problems are derived, and it is shown that optimal feature selection can be posed, in dual form, as ...

Machine learning models are successfully being used for problems in language, vision, and biology that have millions or tens of millions of features. A common approach to alleviating the complexity of high dimensional feature spaces is to penalize the L1 or L2 norm of the parameter vector. We may be able to design more effective regularizers, though, if we possess external information about whi...

NLP models have many and sparse features, and regularization is key for balancing model overfitting versus underfitting. A recently repopularized form of regularization is to generate fake training data by repeatedly adding noise to real data. We reinterpret this noising as an explicit regularizer, and approximate it with a second-order formula that can be used during training without actually ...

Feature extraction is a commonly used technique applied before classification when a number of measures, or features, have been taken from a set of objects in a typical statistical pattern recognition task. The goal is to define a mapping from the original representation space into a new space where the classes are more easily separable. This will reduce the classifier complexity, increasing in...

We extend the well-known technique of constrained Maximum Likelihood Linear Regression (MLLR) to compute a projection (instead of a full rank transformation) on the feature vectors of the adaptation data. We model the projected features with phone-dependent Gaussian distributions and also model the complement of the projected space with a single class-independent, speaker-specific Gaussian dist...