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  "Title": "Determining the Number of Factors in Exploratory Factor Analysis\nby LSTM",
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  "Author": "Haijiang Qin [aut, cre, cph] (ORCID:\n<https://orcid.org/0009-0000-6721-5653>), Lei Guo [aut, cph]\n(ORCID: <https://orcid.org/0000-0002-8273-3587>)",
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  "Description": "A method for factor retention using a pre-trained Long\nShort Term Memory (LSTM) Network, which is originally developed\nby Hochreiter and Schmidhuber (1997)\n<doi:10.1162/neco.1997.9.8.1735>, is provided. The sample size\nof the dataset used to train the LSTM model is 1,000,000. Each\nsample is a batch of simulated response data with a specific\nlatent factor structure. The eigenvalues of these response data\nwill be used as sequential data to train the LSTM. The\npre-trained LSTM is capable of factor retention for real\nresponse data with a true latent factor number ranging from 1\nto 10, that is, determining the number of factors.",
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