Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Rank-One Approximation to High Order Tensors
SIAM Journal on Matrix Analysis and Applications
Orthogonal Tensor Decompositions
SIAM Journal on Matrix Analysis and Applications
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
CubeSVD: a novel approach to personalized Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Rank-R Approximation of Tensors: Using Image-as-Matrix Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Relation between PLSA and NMF and implications
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Non-negative tensor factorization with applications to statistics and computer vision
ICML '05 Proceedings of the 22nd international conference on Machine learning
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Simultaneous Component and Clustering Models for Three-way Data: Within and Between Approaches
Journal of Classification
Computational Statistics & Data Analysis
Author-topic evolution analysis using three-way non-negative Paratucker
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Temporal Analysis of Semantic Graphs Using ASALSAN
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Clustering based on matrix approximation: a unifying view
Knowledge and Information Systems
Probabilistic polyadic factorization and its application to personalized recommendation
Proceedings of the 17th ACM conference on Information and knowledge management
Clustering multi-way data via adaptive subspace iteration
Proceedings of the 17th ACM conference on Information and knowledge management
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Convex and Semi-Nonnegative Matrix Factorizations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling and multiway analysis of chatroom tensors
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
Deflation-based power iteration clustering
Applied Intelligence
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Non-negative Matrix Factorization (NMF) and Probabilistic Latent Semantic Analysis (PLSA) are two widely used methods for non-negative data decomposition of two-way data (e.g., document-term matrices). Studies have shown that PLSA and NMF (with the Kullback-Leibler divergence objective) are different algorithms optimizing the same objective function. Recently, analyzing multi-way data (i.e., tensors), has attracted a lot of attention as multi-way data have rich intrinsic structures and naturally appear in many real-world applications. In this paper, the relationships between NMF and PLSA extensions on multi-way data, e.g., NTF (Non-negative Tensor Factorization) and T-PLSA (Tensorial Probabilistic Latent Semantic Analysis), are studied. Two types of T-PLSA models are shown to be equivalent to two well-known non-negative factorization models: PARAFAC and Tucker3 (with the KL-divergence objective). NTF and T-PLSA are also compared empirically in terms of objective functions, decomposition results, clustering quality, and computation complexity on both synthetic and real-world datasets. Finally, we show that a hybrid method by running NTF and T-PLSA alternatively can successfully jump out of each other's local minima and thus be able to achieve better clustering performance.