Probabilistic Rank-One Tensor Analysis With Concurrent Regularizations
نویسندگان
چکیده
منابع مشابه
Probabilistic Rank-One Matrix Analysis with Concurrent Regularization
As a classical subspace learning method, Probabilistic PCA (PPCA) has been extended to several bilinear variants for dealing with matrix observations. However, they are all based on the Tucker model, leading to a restricted subspace representation and the problem of rotational ambiguity. To address these problems, this paper proposes a bilinear PPCA method named as Probabilistic Rank-One Matrix...
متن کاملExploiting tensor rank-one decomposition in probabilistic inference
We propose a new additive decomposition of probability tables – tensor rank-one decomposition. The basic idea is to decompose a probability table into a series of tables, such that the table that is the sum of the series is equal to the original table. Each table in the series has the same domain as the original table but can be expressed as a product of one-dimensional tables. Entries in table...
متن کاملThe Geometry of Rank-one Tensor Completion
The geometry of the set of restrictions of rank-one tensors to some of their coordinates is studied. This gives insight into the problem of rank-one completion of partial tensors. Particular emphasis is put on the semialgebraic nature of the problem, which arises for real tensors with constraints on the parameters. The algebraic boundary of the completable region is described for tensors parame...
متن کاملTensor rank-one decomposition of probability tables
We propose a new additive decomposition of probability tables tensor rank-one decomposition. The basic idea is to decompose a probability table into a series of tables, such that the table that is the sum of the series is equal to the original table. Each table in the series has the same domain as the original table but can be expressed as a product of one-dimensional tables. Entries in tables ...
متن کاملTensor Sketching: Sparsification and Rank-One Projection
In this paper, we investigate effective sketching schemes for high dimensional multilinear arrays or tensors. More specifically, we propose a novel tensor sparsification algorithm that retains a subset of the entries of a tensor in a judicious way, and prove that it can attain a given level of approximation accuracy in terms of tensor spectral norm with a much smaller sample complexity when com...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Cybernetics
سال: 2021
ISSN: 2168-2267,2168-2275
DOI: 10.1109/tcyb.2019.2914316