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May 21, 2016 · The proposed approach leverages matrix and tensor factorization models that produce essentially unique latent representations of the data to ...
This paper proposes a joint factor analysis and latent clustering framework, which aims at learning cluster-aware low-dimensional representations of matrix ...
Abstract—Many real-life datasets exhibit structure in the form of physically meaningful clusters - e.g., news documents can be.
Missing: Hidden Traits:
Abstract—Dimensionality reduction techniques play an essen- tial role in data analytics, signal processing, and machine learning.
Mar 17, 2016 · Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering by. Professor Nikos Sidiropoulos. Dept. of Electrical & Computer ...
Learning from hidden traits: Joint factor analysis and latent clustering. B Yang, X Fu, ND Sidiropoulos. IEEE Transactions on Signal Processing 65 (1), 256-269, ...
Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering · pdf ... Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering · pdf ...
"Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering'', IEEE Trans. on Signal Processing, vol. 65, no. 1, pp. 256--269, Jan. 2017. B ...
Learning from hidden traits: Joint factor analysis and latent clustering. B Yang, X Fu, ND Sidiropoulos. IEEE Transactions on Signal Processing 65 (1), 256-269, ...