data.datasets.LSTM

<p>This dataset is a subset of the full datasets, consisting of 1,000 samples
from the original 1,000,000-sample datasets.</p>

A method for factor retention using a pre-trained Long Short Term Memory (LSTM) Network,
which is originally developed by
Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>, is provided.
The sample size of the dataset used to train the LSTM model is 1,000,000.
Each sample is a batch of simulated response data with a specific latent factor structure.
The eigenvalues of these response data will be used as sequential data to train the LSTM.
The pre-trained LSTM is capable of factor retention for real response data with a
true latent factor number ranging from 1 to 10, that is, determining the number of factors.

Haijiang Qin

LSTMfactors

Determining the Number of Factors in Exploratory Factor Analysis
by LSTM

Lei Guo

data.datasets.LSTM function

<p>A 1,000×21 matrix, where the firt column represents the labels and
the last 20 columns represent feature values, which correspond to the number of
factors associated with the features.</p>

data.datasets.LSTM: Subset Dataset for Training the Pre-Trained Long Short Term Memory (LSTM) Network

Description

Arguments

Format

See Also

Examples