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A Novel Data Instance Reduction Technique using Linear Feature Reduction

Arpita Joshi1, 2, * Nurit Haspel2

Corresponding Author:

Arpita Joshi


1 Department of Computer Science, Purdue University Northwest, Hammond, IN, USA
Email: [email protected]/[email protected]
2 Department of Computer Science, University of Massachusetts, Boston, MA, USA
Email: [email protected]
* Corresponding Author: Arpita Joshi, Email: [email protected]


Representation of structurally significant data is indispensable to modern research. The need for dimensionality reduction finds its foray in varied genres viz-a-viz, Structural Bioinformatics, Machine Learning, Robotics, Artificial Intelligence, to name a few. The number of points required to effectively capture the essence of a structure is an intuitive decision. Feature reduction methods like Principal Component Analysis (PCA) have already been explored and proven to be an aid in classification and regression. In this work we present a novel approach that first performs PCA on a data set for reduction of features and then attempts to reduce the number of points itself to get rid of the points that have nothing or very little new to offer. The algorithm was tested on various kinds of data (points representing a spiral, protein coordinates, the Iris dataset prevalent in Machine Learning, face image) and the results agree with the quantitative tests applied. In each case, it turns out that a lot of data instances need not be stored to make any kind of decision. Matlab and R simulations were used to assess the structures with reduced data points. The time complexity of the algorithm is linear in the degrees of freedom of the data if the data is in a natural order.


Dimensionality Reduction, PCA, Protein Conformations, Non-linear feature reduction, Data instance reduction

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Cite This Paper:

Arpita Joshi and Nurit Haspel (2020). A Novel Data Instance Reduction Technique using Linear Feature Reduction. Journal of Artificial Intelligence and Systems, 2, 191–206.


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