  Convolutional neural networks (CNNs) have been successful in representing the
fully-connected inferencing ability perceived to be seen in the human brain:
they take full advantage of the hierarchy-style patterns commonly seen in
complex data and develop more patterns using simple features. Countless
implementations of CNNs have shown how strong their ability is to learn these
complex patterns, particularly in the realm of image classification. However,
the cost of getting a high performance CNN to a so-called "state of the art"
level is computationally costly. Even when using transfer learning, which
utilize the very deep layers from models such as MobileNetV2, CNNs still take a
great amount of time and resources. Linear discriminant analysis (LDA), a
generalization of Fisher's linear discriminant, can be implemented in a
multi-class classification method to increase separability of class features
while not needing a high performance system to do so for image classification.
Similarly, we also believe LDA has great promise in performing well. In this
paper, we discuss our process of developing a robust CNN for food
classification as well as our effective implementation of multi-class LDA and
prove that (1) CNN is superior to LDA for image classification and (2) why LDA
should not be left out of the races for image classification, particularly for
binary cases.
