An Actual Survey of Dimensionality Reduction
نویسندگان
چکیده
منابع مشابه
An Actual Survey of Dimensionality Reduction
Dimension reduction is defined as the processes of projecting high-dimensional data to a much lower-dimensional space. Dimension reduction methods variously applied in regression, classification, feature analysis and visualization. In this paper, we review in details the last and most new version of methods that extensively developed in the past decade.
متن کاملA survey of dimensionality reduction techniques
—Experimental life sciences like biology or chemistry have seen in the recent decades an explosion of the data available from experiments. Laboratory instruments become more and more complex and report hundreds or thousands measurements for a single experiment and therefore the statistical methods face challenging tasks when dealing with such high‐dimensional data. However, much of the data is ...
متن کاملGraph Embedding and Dimensionality Reduction - A Survey
Dimension reduction is defined as the process of mapping high-dimensional data to a lowerdimensional vector space. Most machine learning and data mining techniques may not be effective for high-dimensional data. In order to handle this data adequately, its dimensionality needs to be reduced. Dimensionality reduction is also needed for visualization, graph embedding, image retrieval and a variet...
متن کاملLinear dimensionality reduction: survey, insights, and generalizations
Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional data, due to their simple geometric interpretations and typically attractive computational properties. These methods capture many data features of interest, such as covariance, dynamical structure, correlation between data sets, input-output relationships, and margin between data classes. Methods have been d...
متن کاملDimensionality Reduction for Language A Survey of Dimensionality Reduction Techniques for Natural Language
Machine learning methods for natural language use features consisting of words or combinations of words to fit statistical models of linguistic phenomena. The discrete input spaces resulting from these features often have hundreds of thousands or millions of dimensions, and estimating reliable statistics of these features from limited amounts of training data is difficult. One technique for all...
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: American Journal of Computational Mathematics
سال: 2014
ISSN: 2161-1203,2161-1211
DOI: 10.4236/ajcm.2014.42006