ArticleJournal of machine learning research : JMLR2020
Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality.
Article in Journal of machine learning research : JMLR, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- NETWORK-BASED MODELING OF EMOTIONAL EXPRESSIONS FOR MULTIPLE CANCERS VIA A LINGUISTIC ANALYSIS OF AN ONLINE HEALTH COMMUNITY.The annals of applied statistics · 2025Article
- Mode-wise principal subspace pursuit and matrix spiked covariance model.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- Joint tensor modeling of single cell 3D genome and epigenetic data with Muscle.Journal of the American Statistical Association · 2024Article
- Partially Observed Dynamic Tensor Response Regression.Journal of the American Statistical Association · 2023Article
- Sparse Group Lasso: Optimal Sample Complexity, Convergence Rate, and Statistical Inference.IEEE transactions on information theory · 2022Article
- Reproducibility in Matrix and Tensor Decompositions: Focus on Model Match, Interpretability, and Uniqueness.IEEE signal processing magazine · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We consider the problem of decomposing a higher-order tensor with binary entries. Such data problems arise frequently in applications such as neuroimaging, recommendation system, topic modeling, and sensor network localization. We propose a multilinear Bernoulli model, develop a rank-constrained likelihood-based estimation method, and obtain the theoretical accuracy guarantees. In contrast to continuous-valued problems, the binary tensor problem exhibits an interesting phase transition phenomenon according to the signal-to-noise ratio. The error bound for the parameter tensor estimation is established, and we show that the obtained rate is minimax optimal under the considered model. Furthermore, we develop an alternating optimization algorithm with convergence guarantees. The efficacy of our approach is demonstrated through both simulations and analyses of multiple data sets on the tasks of tensor completion and clustering.
Indexed as
Identifiers
34557057PMC8457422What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.