Dimensionality reduction in neuroscience
نویسندگان
چکیده
منابع مشابه
Dimensionality reduction in neuroscience
The nervous system extracts information from its environment and distributes and processes that information to inform and drive behaviour. In this task, the nervous system faces a type of data analysis problem, for, while a visual scene may be overflowing with information, reaching for the television remote before us requires extraction of only a relatively small fraction of that information. W...
متن کاملDimensionality Reduction
Dimensionality reduction studies methods that effectively reduce data dimensionality for efficient data processing tasks such as pattern recognition, machine learning, text retrieval, and data mining. We introduce the field of dimensionality reduction by dividing it into two parts: feature extraction and feature selection. Feature extraction creates new features resulting from the combination o...
متن کامل2D Dimensionality Reduction Methods without Loss
In this paper, several two-dimensional extensions of principal component analysis (PCA) and linear discriminant analysis (LDA) techniques has been applied in a lossless dimensionality reduction framework, for face recognition application. In this framework, the benefits of dimensionality reduction were used to improve the performance of its predictive model, which was a support vector machine (...
متن کاملDimensionality reduction in Hilbert spaces
Dimensionality reduction is a generic name for any procedure that takes a complicated object living in a high-dimensional (or possibly even infinite-dimensional) space and approximates it in some sense by a finite-dimensional vector. We are interested in a particular class of dimensionality reduction methods. Consider a data source that generates vectors in some Hilbert space H , which is eithe...
متن کاملDimensionality Reduction in Complex Models
As a part of the Managing Uncertainty in Complex Models (MUCM) project, research at Aston University will develop methods for dimensionality reduction of the input and/or output spaces of models, as seen within the emulator framework. Towards this end this report describes a framework for generating toy datasets, whose underlying structure is understood, to facilitate early investigations of di...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Current Biology
سال: 2016
ISSN: 0960-9822
DOI: 10.1016/j.cub.2016.05.029