Analysis of Molecular Motion using Non-Linear Dimensionality

tion using principal feature analysis, submitted to icip'02, 2002.

How many samples are sufficient to guarantee that the eigenvectors of the sample covariance matrix are close to those of the actual covariance matrix? For a wide family of distributions, including distributions with finite second moment and sub-gaussian distributions supported in a centered Euclidean ball, we prove that the inner product between eigenvectors of the sample and actual covariance ...

Many different evaluation measures for dimensionality reduction can be summarized based on the co-ranking framework [6]. Here, we extend this framework in two ways: (i) we show that the current parameterization of the quality shows unpredictable behavior, even in simple settings, and we propose a different parameterization which yields more intuitive results; (ii) we propose how to link the qua...

Article history: Available online 29 October 2011

Article history: Available online 2 July 2009

We present a comprehensive analysis of the use of I-vector based classifiers for the classification of unlabelled acoustic data as native British accents. We demonstrate the different behaviours of various popular dimensionality reduction techniques that have been previously used in problems such as speaker and language classification. Our results show that a fusion of I-vector based systems gi...