In the area of dimensionality reduction, principal component analysis (PCA) has been used with much success. Other dimensionality reduction techniques have been proposed such as principal feature analysis (PFA) which was developed by Ira Cohen, Qi Tian et.al. PFA uses k-means clustering with the principal components to determine principal features. We present a new approach to dimensionality reduction of features called Summed Component Analysis (SCA). SCA uses similar criteria as PFA and PCA to create a lower dimensional feature space. However, it is unique in the way the features in the new space are formed by summing selected features from the original space. The simplicity of the approach lends some advantages to analysis since the new features are simply sums of a selected number of the original features in the lower dimensional space. Furthermore, with SCA we are able to show improved classification performance over PCA, which is known to give impressive lower-dimensional representation of a dataset, but which doesn’t always translate to improvement in classification. SCA can prove useful when applied to high dimensional data sets to be classified, such as physical measurements that describe different scenarios, or in the area of financial data analysis in which different stocks are to be combined in a way that provide optimal information needed to classify stock market trends.

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