2,973 research outputs found
Zero-Truncated Poisson Tensor Factorization for Massive Binary Tensors
We present a scalable Bayesian model for low-rank factorization of massive
tensors with binary observations. The proposed model has the following key
properties: (1) in contrast to the models based on the logistic or probit
likelihood, using a zero-truncated Poisson likelihood for binary data allows
our model to scale up in the number of \emph{ones} in the tensor, which is
especially appealing for massive but sparse binary tensors; (2)
side-information in form of binary pairwise relationships (e.g., an adjacency
network) between objects in any tensor mode can also be leveraged, which can be
especially useful in "cold-start" settings; and (3) the model admits simple
Bayesian inference via batch, as well as \emph{online} MCMC; the latter allows
scaling up even for \emph{dense} binary data (i.e., when the number of ones in
the tensor/network is also massive). In addition, non-negative factor matrices
in our model provide easy interpretability, and the tensor rank can be inferred
from the data. We evaluate our model on several large-scale real-world binary
tensors, achieving excellent computational scalability, and also demonstrate
its usefulness in leveraging side-information provided in form of
mode-network(s).Comment: UAI (Uncertainty in Artificial Intelligence) 201
Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition
Product reviews and ratings on e-commerce websites provide customers with
detailed insights about various aspects of the product such as quality,
usefulness, etc. Since they influence customers' buying decisions, product
reviews have become a fertile ground for abuse by sellers (colluding with
reviewers) to promote their own products or to tarnish the reputation of
competitor's products. In this paper, our focus is on detecting such abusive
entities (both sellers and reviewers) by applying tensor decomposition on the
product reviews data. While tensor decomposition is mostly unsupervised, we
formulate our problem as a semi-supervised binary multi-target tensor
decomposition, to take advantage of currently known abusive entities. We
empirically show that our multi-target semi-supervised model achieves higher
precision and recall in detecting abusive entities as compared to unsupervised
techniques. Finally, we show that our proposed stochastic partial natural
gradient inference for our model empirically achieves faster convergence than
stochastic gradient and Online-EM with sufficient statistics.Comment: Accepted to the 25th ACM SIGKDD Conference on Knowledge Discovery and
Data Mining, 2019. Contains supplementary material. arXiv admin note: text
overlap with arXiv:1804.0383
Incremental multi-domain learning with network latent tensor factorization
The prominence of deep learning, large amount of annotated data and
increasingly powerful hardware made it possible to reach remarkable performance
for supervised classification tasks, in many cases saturating the training
sets. However the resulting models are specialized to a single very specific
task and domain. Adapting the learned classification to new domains is a hard
problem due to at least three reasons: (1) the new domains and the tasks might
be drastically different; (2) there might be very limited amount of annotated
data on the new domain and (3) full training of a new model for each new task
is prohibitive in terms of computation and memory, due to the sheer number of
parameters of deep CNNs. In this paper, we present a method to learn
new-domains and tasks incrementally, building on prior knowledge from already
learned tasks and without catastrophic forgetting. We do so by jointly
parametrizing weights across layers using low-rank Tucker structure. The core
is task agnostic while a set of task specific factors are learnt on each new
domain. We show that leveraging tensor structure enables better performance
than simply using matrix operations. Joint tensor modelling also naturally
leverages correlations across different layers. Compared with previous methods
which have focused on adapting each layer separately, our approach results in
more compact representations for each new task/domain. We apply the proposed
method to the 10 datasets of the Visual Decathlon Challenge and show that our
method offers on average about 7.5x reduction in number of parameters and
competitive performance in terms of both classification accuracy and Decathlon
score.Comment: AAAI2
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