A major challenge for cardiac motion analysis is the highdimensionality of the motion data. Conventionally, the AHA model is used for dimensionality reduction, which divides the left ventricle into 17 segments using criteria based on anatomical structures. In this paper, a novel method is proposed to divide the left ventricle into homogeneous parcels in terms of motion trajectories. We demonstr...

High dimensional engineered features have yielded high performance results on a variety of visual recognition tasks and attracted significant recent attention. Here, we examine the problem of expression recognition in static facial images. We first present a technique to build high dimensional, ∼60k features composed of dense Census transformed vectors based on locations defined by facial keypo...

Article history: Received 9 July 2008 Received in revised form 9 December 2008 Accepted 12 December 2008

In the area of pattern recognition, it is common for few training samples to be available with respect to the dimensionality of the representation space; this is known as the curse of dimensionality. This problem can be alleviated by using a dimensionality reduction approach, which overcomes the curse relatively well. Moreover, supervised dimensionality reduction techniques generally provide be...

“Curse of dimensionality” has been a significant obstacle to solving many problems. One way to avoid this obstacle is to use dimensionality reduction methods to reduce the dimension of the data while preserving the properties of the data. Reader is referred to [Saul, 2005, Fodor, 2002] for a detailed review of these dimensionality reduction methods. Almost all of these dimensionality reduction ...

How can we search for low dimensional structure in high dimensional data? If the data is mainly confined to a low dimensional subspace, then simple linear methods can be used to discover the subspace and estimate its dimensionality. More generally, though, if the data lies on (or near) a low dimensional submanifold, then its structure may be highly nonlinear, and linear methods are bound to fai...

Dimensionality reduction is one of the basic operations in the toolbox of data-analysts and designers of machine learning and pattern recognition systems. Given a large set of measured variables but few observations, an obvious idea is to reduce the degrees of freedom in the measurements by representing them with a smaller set of more “condensed” variables. Another reason for reducing the dimen...

Dimensionality reduction procedures such as principal component analysis and the maximum margin criterion discriminant are special cases of a weighted maximum variance (WMV) approach. We present a simple two parameter version of WMV that we call 2P-WMV. We study the classification error given by the 1-nearest neighbor algorithm on features extracted by our and other dimensionality reduction met...

Spectral clustering is a flexible clustering methodology that is applicable to a variety of data types and has the particular virtue that it makes few assumptions on cluster shapes. It has become popular in a variety of application areas, particularly in computational vision and bioinformatics. The approach appears, however, to be particularly sensitive to irrelevant and noisy dimensions in the...

The demonstration presents the Matlab Toolbox for Dimensionality Reduction. The toolbox is publicly available and contains implementations of virtually all state-of-the-art techniques for dimensionality reduction and intrinsic dimensionality estimation. It provides implementations of 27 techniques for dimensionality reduction, 6 techniques for intrinsic dimensionality estimation, and additional...