Unsupervised Ensemble Learning Improves Discriminability of Stochastic Neighbor Embedding
Tóm tắt
Từ khóa
Tài liệu tham khảo
Goodfellow, I.J., Erhan, D., Carrier, P.L., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., Lee, D.-H., et al.: Challenges in representation learning: A report on three machine learning contests. In: International Conference on Neural Information Processing, pp. 117–124 (2013). Springer
Hong, D., Hu, J., Yao, J., Chanussot, J., Zhu, X.X.: Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model. ISPRS J. Photogramm. Remote. Sens. 178, 68–80 (2021)
Chen, J., Wang, Y., Zhang, L., Liu, M., Plaza, A.: Drfl-vat: Deep representative feature learning with virtual adversarial training for semi-supervised classification of hyperspectral image. IEEE Trans. Geosci. Remote Sens. (2022)
Peng, B., Lei, J., Fu, H., Jia, Y., Zhang, Z., Li, Y.: Deep video action clustering via spatio-temporal feature learning. Neurocomputing 456, 519–527 (2021)
Liu, Z., Wang, R., Japkowicz, N., Tang, D., Zhang, W., Zhao, J.: Research on unsupervised feature learning for android malware detection based on restricted boltzmann machines. Fut. Gen. Comput. Syst. 120, 91–108 (2021)
Izenman, A.J.: Introduction to manifold learning. Wiley Interdiscip. Rev. Comput. Stat. 4(5), 439–446 (2012)
Zhao, H., Wang, H.-J., Peng, B., Long, Z.-G., Li, T.-R.: Discriminative feature learning based on stochastic neighbor embedding. J. Softw. 33(4), 1326–1337 (2022)
Karamizadeh, S., Abdullah, S.M., Manaf, A.A., Zamani, M., Hooman, A.: An overview of principal component analysis. J.Signal Inf. Process. 4 (2020)
Pohar, M., Blas, M., Turk, S.: Comparison of logistic regression and linear discriminant analysis: a simulation study. Metodoloski zvezki 1(1), 143 (2004)
Yi, S., Lai, Z., He, Z., Cheung, Y.-M., Liu, Y.: Joint sparse principal component analysis. Pattern Recogn. 61, 524–536 (2017)
Ramirez-Figueroa, J.A., Martin-Barreiro, C., Nieto-Librero, A.B., Leiva, V., Galindo-Villardón, M.P.: A new principal component analysis by particle swarm optimization with an environmental application for data science. Stoch. Env. Res. Risk Assess. 35(10), 1969–1984 (2021)
Ioffe, S.: Probabilistic linear discriminant analysis. In: European Conference on Computer Vision, pp. 531–542 (2006)
Bahraini, T., Hosseini, S.M., Ghasempour, M., Yazdi, H.S.: Density-oriented linear discriminant analysis. Expert Syst. Appl. 187, 115946 (2022)
Schölkopf, B., Smola, A., Müller, K.-R.: Kernel principal component analysis. In: International Conference on Artificial Neural Networks, pp. 583–588 (1997)
Müller, K.-R., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B.: An introduction to kernel-based learning algorithms. In: Handbook of Neural Network Signal Processing: Neural Network Signal Processing, pp. 95–134 (2002)
Arbab, F., Herman, I., Spilling, P.: An overview of manifold and its implementation. Concurr. Pract. Exp. 5(1), 23–70 (1993)
Tenenbaum, J.B., Silva, V.D., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
Jaworska, N., Chupetlovska-Anastasova, A.: A review of multidimensional scaling (mds) and its utility in various psychological domains. Tutor Quant. Methods Psychol. 5(1), 1–10 (2009)
Zhang, X.-H., Xu, Y., He, Y.-L., Zhu, Q.-X.: Novel manifold learning based virtual sample generation for optimizing soft sensor with small data. ISA Trans. 109, 229–241 (2021)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Donoho, D.L., Grimes, C.: Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. Proc. Natl. Acad. Sci. 100(10), 5591–5596 (2003)
Hinton, G.E., Roweis, S.: Stochastic neighbor embedding. Adv. Neural. Inf. Process. Syst. 15, 857–864 (2002)
Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11), 2579–2605 (2008)
Zhou, X., He, J., Yang, C.: An ensemble learning method based on deep neural network and group decision making. Knowl.-Based Syst. 239, 107801 (2022)
Goel, K., Batra, S.: Two-level pruning based ensemble with abstained learners for concept drift in data streams. Expert. Syst. 38(3), 12661 (2021)
Strehl, A., Ghosh, J.: Cluster ensembles–a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2002)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)
Likas, A., Vlassis, N., Verbeek, J.J.: The global k-means clustering algorithm. Pattern Recogn. 36(2), 451–461 (2003)
Park, H.-S., Jun, C.-H.: A simple and fast algorithm for k-medoids clustering. Expert Syst. Appl. 36(2), 3336–3341 (2009)
Qian, N.: On the momentum term in gradient descent learning algorithms. Neural Netw. 12(1), 145–151 (1999)
Fu, Z., Wang, H.-J., Li, T.-R., Teng, F., Zhang, J.: A weakly supervised learning framework based on k labeled samples. J. Softw. 31(4), 981–990 (2020)
Chu, J., Wang, H., Meng, H., Jin, P., Li, T.: Restricted boltzmann machines with gaussian visible units guided by pairwise constraints. IEEE Trans. Cybern. 49(12), 4321–4334 (2018)
Huang, S., Wang, H., Li, T., Yang, Y., Li, T.: Constraint co-projections for semi-supervised co-clustering. IEEE Trans. Cybern. 46(12), 3047–3058 (2015)