An apparatus for classifying an input includes a classifier and a feature extractor. The feature extractor is configured to generate a feature vector based on the input. The feature vector is also configured to set a number of elements of the feature vector to zero to produce a sparse feature vector. The sparse feature vector has the same dimensions as the feature vector generated by the feature extractor. However, the sparse feature vector includes fewer non-zero elements than the feature vector generated by the feature extractor. The feature vector is further configured to forward the sparse feature vector to the classifier to classify the input.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An apparatus for classifying an input, comprising: a classifier; and a feature extractor, the feature extractor configured: to generate a feature vector from the input; to set a number of elements of the feature vector to zero to produce a sparse feature vector, the sparse feature vector having same dimensions as the generated feature vector and including fewer non-zero elements than the generated feature vector; and to forward the sparse feature vector to the classifier to classify the input. 2. The apparatus of claim 1, in which the feature extractor is further configured to determine the number of elements based at least in part on a performance metric. 3. The apparatus of claim 2, in which the feature extractor is further configured to compute the performance metric on-device. 4. The apparatus of claim 2, in which the feature extractor is further configured to compute the performance metric off-device. 5. The apparatus of claim 1, in which the feature extractor is trained to determine the number of elements of the feature vector to retain. 6. The apparatus of claim 5, in which the feature extractor is trained using a cost function that penalizes low sparsity of the feature vector. 7. The apparatus of claim 6, in which the cost function includes an L1 norm or L0 norm. 8. The apparatus of claim 1, in which the feature extractor is further configured to binarize elements of the sparse feature vector. 9. The apparatus of claim 1, in which the feature extractor is further configured to quantize elements of the sparse feature vector. 10. A method for classifying an input, comprising: generating a feature vector from the input; setting a number of elements of the feature vector to zero to produce a sparse feature vector, the sparse feature vector having same dimensions as the generated feature vector and including fewer non-zero elements than the generated feature vector; and forwarding the sparse feature vector to a classifier to classify the input. 11. The method of claim 10, further comprising determining the number of elements based at least in part on a performance metric. 12. The method of claim 11, further comprising computing the performance metric on-device. 13. The method of claim 11, further comprising computing the performance metric off-device. 14. The method of claim 10, further comprising determining the number of elements of the feature vector to retain. 15. The method of claim 14, in which the determining comprises training a feature extractor using a cost function that penalizes low sparsity of the feature vector. 16. The method of claim 15, in which the cost function includes an L1 norm or L0 norm. 17. The method of claim 10, further comprising binarizing elements of the sparse feature vector. 18. The method of claim 10, further comprising quantizing elements of the sparse feature vector. 19. An apparatus for classifying an input, comprising: means for generating a feature vector from the input; means for setting a number of elements of the feature vector to zero to produce a sparse feature vector, the sparse feature vector having same dimensions as the generated feature vector and including fewer non-zero elements than the generated feature vector; and means for forwarding the sparse feature vector to a classifier to classify the input. 20. A non-transitory computer-readable medium having encoded thereon program code for classifying an input, the program code being executed by a processor and comprising: program code to generate a feature vector from the input; program code to set a number of elements of the feature vector to zero to produce a sparse feature vector, the sparse feature vector having same dimensions as the generated feature vector and including fewer non-zero elements than the generated feature vector; and program code to forward the sparse feature vector to a classifier to classify the input.