An extension of principal component analysis called ipPCA has been proposed earlier for analyzing structure in genetic data. This non-parametric framework iteratively classifies ...
Principal component analysis (PCA) is a classical data analysis technique that finds linear transformations of data that retain the maximal amount of variance. We study a case whe...
In this paper, we propose an unsupervised approach for identifying bipolar person names in a set of topic documents. We employ principal component analysis (PCA) to discover bipol...
Kernel Principal Component Analysis (KPCA) is investigated for feature extraction from hyperspectral remotesensing data. Features extracted using KPCA are used to construct the Ex...
Mathieu Fauvel, Jocelyn Chanussot, Jon Atli Benedi...
Abstract-- Motion planning of deformable objects is challenging due to the high degrees-of-freedom inherent in deformation as well as the computational cost of producing physically...
Numerical Abstract Domains via Principal Component Analysis Gianluca Amato, Maurizio Parton, and Francesca Scozzari Universit`a di Chieti-Pescara – Dipartimento di Scienze We pro...
A novel backwards viewpoint of Principal Component Analysis is proposed. In a wide variety of cases, that fall into the area of Object Oriented Data Analysis, this viewpoint is se...
We present a method for automated topic suggestion. Given a plain-text input document, our algorithm produces a ranking of novel topics that could enrich the input document in a m...
Background: Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique fo...
Gift Nyamundanda, Lorraine Brennan, Isobel Claire ...
This paper proposes a statistical, non-feature based, attention mechanism for a mobile robot, termed Information Sampling. The selected data may be a single pixel or a number scat...