Titulo:
El problema de la reducción dimensional. Análisis de Componentes Principales (PCA)
.
Sumario:
En este trabajo de investigación se presenta la técnica de Principal Component Analysis (PCA), y su aplicación práctica al aprendizaje automático (machine learning). La intención es abordar la problemática de la reducción dimensional o compresión de datos. A partir de un análisis intuitivo, se espera acercar a los economistas y otros profesionales de las ciencias sociales estas ideas que, generalmente, resultan ajenas a sus discusiones.
Guardado en:
2256-1498
14
2024-01-01
1
21
Revista Mutis - 2023
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El problema de la reducción dimensional. Análisis de Componentes Principales (PCA) The Problem of Dimensional Reduction. Principal Component Analysis (PCA) En este trabajo de investigación se presenta la técnica de Principal Component Analysis (PCA), y su aplicación práctica al aprendizaje automático (machine learning). La intención es abordar la problemática de la reducción dimensional o compresión de datos. A partir de un análisis intuitivo, se espera acercar a los economistas y otros profesionales de las ciencias sociales estas ideas que, generalmente, resultan ajenas a sus discusiones. This research paper presents the Principal Component Analysis (PCA) technique, and its practical application to machine learning. The objective is to address the problem of dimensional reduction or data compression. Based on an intuitive analysis, this document will help bring economists and other professionals in the social sciences closer to these ideas, which are generally alien to their discussions. Pernice , Sergio A. análisis de componentes principales aprendizaje no supervisado reducción dimensional ingeniería Principal component analysis Unsupervised learning Dimensional reduction Engineering 14 1 Artículo de revista Journal article 2024-01-01T00:00:00Z 2024-01-01T00:00:00Z 2024-01-01 application/pdf Universidad de Bogotá Jorge Tadeo Lozano Revista Mutis 2256-1498 https://revistas.utadeo.edu.co/index.php/mutis/article/view/problema-reduccion-dimensional-analisis-componentes-principales-PCA 10.21789/22561498.2057 https://doi.org/10.21789/22561498.2057 spa https://creativecommons.org/licenses/by-nc-sa/4.0 Revista Mutis - 2023 Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0. 1 21 Athey-Imbens. (2019).Susan Athey and GuidoW. Imbens, “Machine Learning Methods That Economists Should Know About”, Annual Review of Economics 2019 11:1, 685-725. https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-080217-053433. Bai-Ng, Bai, J. and Ng, S. (2004). “A PANIC Attack on Unit Roots and Cointegration”. Econometrica, 72: 1127-1177. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1468-0262.2004.00528.x. Bai-Ng. (2007). Bai, Jushan, and Serena Ng. “Determining the Number of Primitive Shocks in Factor Models.” Journal of Business & Economic Statistics 25, no. 1 (2007): 52–60. http://www.jstor.org/stable/27638906. Bai-Ng. (2017). Bai, J. and Ng, S.. “Principal components and regularized estimation of factor models”, https://arxiv.org/abs/1708.08137. Bates, B. J., M. PLAGBORG-MoLLER-Stock-Watson, J. H. Stock, AND M.W.Watson. (2013). “Consistent Factor Estimation in Dynamic Factor Models with Structural Instability,” Journal of Econometrics, V. 177 (2), p. 289-304, 2013. https://www. sciencedirect.com/science/article/abs/pii/S0304407613000912?via%3Dihub. Ben S. Bernanke, Jean Boivin, Piotr Eliasz. (2005). “Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach”, The Quarterly Journal of Economics, Volume 120, Issue 1, February 2005, Pages 387–422. https://doi.org/10.1162/0033553053327452 Cheng, X, Z. Liao, F. Schorfheide. (2016). “Shrinkage Estimation of High- Dimensional Factor Models with Structural Instabilities”, The Review of Economic Studies, Volume 83, Issue 4, October 2016, Pages 1511–1543, https://doi.org/10.1093/restud/rdw005. Chen, L., Dolado, J.J. and Gonzalo, J. (2021). “Quantile Factor Models”. Econometrica, 89: 875-910. https://doi.org/10.3982/ECTA15746. Lettau, M., Markus Pelger. (2020). “Estimating latent asset-pricing factors”, Journal of Econometrics, ISSN: 0304-4076, Vol: 218, Issue: 1, Page: 1-31, 2020. https://doi.org/10.1016/j.jeconom.2019.08.012. Mol, C., Giannone, D., Reichlin, L. (2007). “Forecasting Using a Large Number of Predictors: Is Bayesian Regression a Valid Alternative to Principal Components?”. Journal of Econometrics, Volume 146, Issue 2, Pages 318-328. https://www.sciencedirect.com/science/article/abs/pii/S0304407608001103?via%3Dihub. Stock, J. H., and M. W. Watson. (2002). “Forecasting Using Principal Components from a Large Number of Predictors.” Journal of the American Statistical Association 97, no. 460 (2002): 1167–79. http://www.jstor.org/stable/3085839. Stock, J. H., and M. W. Watson. (2012). “Disentangling the Channels of the 2007-09 Recession,” Brookings Papers on Economic Activity, pp. 81-156. https://www.brookings.edu/bpea-articles/disentangling-the-channels-of-the-2007-2009-recession/20 Vyas, S., L. Kumaranayake. (2006). “Constructing socio-economic status indices: how to use principal components analysis”, Health Policy and Planning, Volume 21, Issue 6, November 2006, Pages 459–468. https://doi.org/10.1093/heapol/czl029. https://revistas.utadeo.edu.co/index.php/mutis/article/download/problema-reduccion-dimensional-analisis-componentes-principales-PCA/2078 info:eu-repo/semantics/article http://purl.org/coar/resource_type/c_6501 http://purl.org/redcol/resource_type/ARTREF info:eu-repo/semantics/publishedVersion http://purl.org/coar/version/c_970fb48d4fbd8a85 info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 Text Publication |
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UNIVERSIDAD JORGE TADEO LOZANO |
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Revista Mutis |
| title |
El problema de la reducción dimensional. Análisis de Componentes Principales (PCA) |
| spellingShingle |
El problema de la reducción dimensional. Análisis de Componentes Principales (PCA) Pernice , Sergio A. análisis de componentes principales aprendizaje no supervisado reducción dimensional ingeniería Principal component analysis Unsupervised learning Dimensional reduction Engineering |
| title_short |
El problema de la reducción dimensional. Análisis de Componentes Principales (PCA) |
| title_full |
El problema de la reducción dimensional. Análisis de Componentes Principales (PCA) |
| title_fullStr |
El problema de la reducción dimensional. Análisis de Componentes Principales (PCA) |
| title_full_unstemmed |
El problema de la reducción dimensional. Análisis de Componentes Principales (PCA) |
| title_sort |
el problema de la reducción dimensional. análisis de componentes principales (pca) |
| title_eng |
The Problem of Dimensional Reduction. Principal Component Analysis (PCA) |
| description |
En este trabajo de investigación se presenta la técnica de Principal Component Analysis (PCA), y su aplicación práctica al aprendizaje automático (machine learning). La intención es abordar la problemática de la reducción dimensional o compresión de datos. A partir de un análisis intuitivo, se espera acercar a los economistas y otros profesionales de las ciencias sociales estas ideas que, generalmente, resultan ajenas a sus discusiones.
|
| description_eng |
This research paper presents the Principal Component Analysis (PCA) technique, and its practical application to machine learning. The objective is to address the problem of dimensional reduction or data compression. Based on an intuitive analysis, this document will help bring economists and other professionals in the social sciences closer to these ideas, which are generally alien to their discussions.
|
| author |
Pernice , Sergio A. |
| author_facet |
Pernice , Sergio A. |
| topicspa_str_mv |
análisis de componentes principales aprendizaje no supervisado reducción dimensional ingeniería |
| topic |
análisis de componentes principales aprendizaje no supervisado reducción dimensional ingeniería Principal component analysis Unsupervised learning Dimensional reduction Engineering |
| topic_facet |
análisis de componentes principales aprendizaje no supervisado reducción dimensional ingeniería Principal component analysis Unsupervised learning Dimensional reduction Engineering |
| citationvolume |
14 |
| citationissue |
1 |
| publisher |
Universidad de Bogotá Jorge Tadeo Lozano |
| ispartofjournal |
Revista Mutis |
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https://revistas.utadeo.edu.co/index.php/mutis/article/view/problema-reduccion-dimensional-analisis-componentes-principales-PCA |
| language |
spa |
| format |
Article |
| rights |
https://creativecommons.org/licenses/by-nc-sa/4.0 Revista Mutis - 2023 Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0. info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 |
| references |
Athey-Imbens. (2019).Susan Athey and GuidoW. Imbens, “Machine Learning Methods That Economists Should Know About”, Annual Review of Economics 2019 11:1, 685-725. https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-080217-053433. Bai-Ng, Bai, J. and Ng, S. (2004). “A PANIC Attack on Unit Roots and Cointegration”. Econometrica, 72: 1127-1177. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1468-0262.2004.00528.x. Bai-Ng. (2007). Bai, Jushan, and Serena Ng. “Determining the Number of Primitive Shocks in Factor Models.” Journal of Business & Economic Statistics 25, no. 1 (2007): 52–60. http://www.jstor.org/stable/27638906. Bai-Ng. (2017). Bai, J. and Ng, S.. “Principal components and regularized estimation of factor models”, https://arxiv.org/abs/1708.08137. Bates, B. J., M. PLAGBORG-MoLLER-Stock-Watson, J. H. Stock, AND M.W.Watson. (2013). “Consistent Factor Estimation in Dynamic Factor Models with Structural Instability,” Journal of Econometrics, V. 177 (2), p. 289-304, 2013. https://www. sciencedirect.com/science/article/abs/pii/S0304407613000912?via%3Dihub. Ben S. Bernanke, Jean Boivin, Piotr Eliasz. (2005). “Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach”, The Quarterly Journal of Economics, Volume 120, Issue 1, February 2005, Pages 387–422. https://doi.org/10.1162/0033553053327452 Cheng, X, Z. Liao, F. Schorfheide. (2016). “Shrinkage Estimation of High- Dimensional Factor Models with Structural Instabilities”, The Review of Economic Studies, Volume 83, Issue 4, October 2016, Pages 1511–1543, https://doi.org/10.1093/restud/rdw005. Chen, L., Dolado, J.J. and Gonzalo, J. (2021). “Quantile Factor Models”. Econometrica, 89: 875-910. https://doi.org/10.3982/ECTA15746. Lettau, M., Markus Pelger. (2020). “Estimating latent asset-pricing factors”, Journal of Econometrics, ISSN: 0304-4076, Vol: 218, Issue: 1, Page: 1-31, 2020. https://doi.org/10.1016/j.jeconom.2019.08.012. Mol, C., Giannone, D., Reichlin, L. (2007). “Forecasting Using a Large Number of Predictors: Is Bayesian Regression a Valid Alternative to Principal Components?”. Journal of Econometrics, Volume 146, Issue 2, Pages 318-328. https://www.sciencedirect.com/science/article/abs/pii/S0304407608001103?via%3Dihub. Stock, J. H., and M. W. Watson. (2002). “Forecasting Using Principal Components from a Large Number of Predictors.” Journal of the American Statistical Association 97, no. 460 (2002): 1167–79. http://www.jstor.org/stable/3085839. Stock, J. H., and M. W. Watson. (2012). “Disentangling the Channels of the 2007-09 Recession,” Brookings Papers on Economic Activity, pp. 81-156. https://www.brookings.edu/bpea-articles/disentangling-the-channels-of-the-2007-2009-recession/20 Vyas, S., L. Kumaranayake. (2006). “Constructing socio-economic status indices: how to use principal components analysis”, Health Policy and Planning, Volume 21, Issue 6, November 2006, Pages 459–468. https://doi.org/10.1093/heapol/czl029. |
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