Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quasi-orthonormal Encoding for Machine Learning Applications   Most machine learning models, especially artificial neural networks, require
numerical, not categorical data. We briefly describe the advantages and
disadvantages of common encoding schemes. For example, one-hot encoding is
commonly used for attributes with a few unrelated categories and word
embeddings for attributes with many related categories (e.g., words). Neither
is suitable for encoding attributes with many unrelated categories, such as
diagnosis codes in healthcare applications. Application of one-hot encoding for
diagnosis codes, for example, can result in extremely high dimensionality with
low sample size problems or artificially induce machine learning artifacts, not
to mention the explosion of computing resources needed. Quasi-orthonormal
encoding (QOE) fills the gap. We briefly show how QOE compares to one-hot
encoding. We provide example code of how to implement QOE using popular ML
libraries such as Tensorflow and PyTorch and a demonstration of QOE to MNIST
handwriting samples.

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The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
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The categories of this paper are: econ
35
The categories of this paper are: mtrl-th