1. A method comprising: receiving a set of features extracted from input data; training a linear classifier based on the set of features extracted; generating a first matrix using the linear classifier, wherein the first matrix includes a first dimension and a second dimension, wherein each dimension includes multiple elements, wherein each element of the first dimension corresponds to a feature of the set of features extracted, and wherein each element of the second dimension corresponds to a classification label of a set of different classification labels for classifying one or more objects of interest in the input data; arranging each element of the second dimension into a corresponding synaptic weight arrangement representing effective synaptic strengths for a classification label corresponding to the element; programming a first neurosynaptic core circuit for dimensionality reduction by programming synaptic weights of a plurality of electronic synapse devices of the first neurosynaptic core circuit based on each synaptic weight arrangement that each element of the second dimension is arranged into; and reducing a number of features included in the first set of features utilizing the first neurosynaptic core circuit, wherein the reduced number of features are mapped to a second neurosynaptic core circuit configured to classify the one or more objects of interest in the input data; wherein each synaptic weight of the synaptic weight arrangement represents a feature associated with a classification label and quantized to a particular value of a range of values to facilitate the dimensionality reduction and the mapping of the reduced number of features to the second neurosynaptic core circuit such that the reduced number of features all fit onto the second neurosynaptic core circuit.