TensorTools (version 1.0.0)

tLDA: Linear discriminate analysis (LDA) on a 3D tensor

Description

Linear discriminate analysis (LDA) on a 3D tensor

Usage

tLDA(tnsr, nClass, nSamplesPerClass, tform)

Value

S3 class tensor

Arguments

tnsr,

a 3-mode tensor S3 class object

nClass,

Number of classes

nSamplesPerClass,

Samples in each class

tform,

Any discrete transform. fft: Fast Fourier Transorm

dwt: Discrete Wavelet Transform (Haar Wavelet)

dct: Discrete Cosine transform

dst: Discrete Sine transform

dht: Discrete Hadley transform

dwht: Discrete Walsh-Hadamard transform

Author

Kyle Caudle

Randy Hoover

Jackson Cates

Everett Sandbo

References

Xanthopoulos, P., Pardalos, P. M., Trafalis, T. B., Xanthopoulos, P., Pardalos, P. M., & Trafalis, T. B. (2013). Linear discriminant analysis. Robust data mining, 27-33.

Examples

data("Mnist")
T <- Mnist$train$images
myorder <- order(Mnist$train$labels)
# tLDA need to be sorted by classes
T_sorted <- T$data[,myorder,]
# Using small tensor, 2 images for each class for demonstration
T <- T_sorted[,c(1:2,1001:1002,2001:2002,3001:3002,4001:4002,
5001:5002,6001:6002,7001:7002,8001:8002,9001:9002),]
tLDA(as.Tensor(T),10,2,"dct")