# Feature Integration Analysis Examples

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AN ASSESSMENT ON FEATURE EXTRACTION APPROACHES C.Sangeetha G.Deepti Raj Assistant Professor Assistant Professor sangsivaselvi@gmail.com deepti.2510@gmail.com Department of Computer Science and Engineering Chettinad College of Engineering & Technology-Karur Abstract: Feature extraction is a special form of dimensionality reduction in data mining, which involves in simplifying the amount of resources required to describe a large set of data accurately. If the features extracted are carefully chosen it is expected that the features set will extract the…show more content…
When the input data to an algorithm is too large to be processed and it is suspected to be notoriously redundant then the input data will be transformed into a reduced representation set of features (also named features vector). Transforming the input data into the set of features is called feature extraction. If the features extracted are carefully chosen it is expected that the features set will extract the relevant information from the input data in order to perform the desired task using this reduced representation instead of the full size…show more content…
FEATURE EXTRACTION APPROACHES: Principal Component Analysis: PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. Technically, a principal component can be defined as a linear combination of optimally-weighted observed variables. In order to understand the meaning of this definition, it is necessary to first describe how subject scores on a principal component are computed. Principal component analysis is sometimes confused with factor analysis, and this is understandable, because there are many important similarities between the two procedures: both are variable reduction methods that can be used to identify groups of observed variables that tend to hang together empirically. Both procedures can be performed with the SAS System’s FACTOR procedure, and they sometimes even provide very similar results. Nonetheless, there are some important conceptual differences between principal component analysis and factor analysis that should be understood at the outset. Perhaps the most important deals with the assumption of an underlying causal structure: factor analysis assumes that the covariation in the observed variables is due to the presence of one or more latent variables (factors) that exert causal influence on these observed