2,282 research outputs found
Efficient Correlated Topic Modeling with Topic Embedding
Correlated topic modeling has been limited to small model and problem sizes
due to their high computational cost and poor scaling. In this paper, we
propose a new model which learns compact topic embeddings and captures topic
correlations through the closeness between the topic vectors. Our method
enables efficient inference in the low-dimensional embedding space, reducing
previous cubic or quadratic time complexity to linear w.r.t the topic size. We
further speedup variational inference with a fast sampler to exploit sparsity
of topic occurrence. Extensive experiments show that our approach is capable of
handling model and data scales which are several orders of magnitude larger
than existing correlation results, without sacrificing modeling quality by
providing competitive or superior performance in document classification and
retrieval.Comment: KDD 2017 oral. The first two authors contributed equall
Learning Topic Models - Going beyond SVD
Topic Modeling is an approach used for automatic comprehension and
classification of data in a variety of settings, and perhaps the canonical
application is in uncovering thematic structure in a corpus of documents. A
number of foundational works both in machine learning and in theory have
suggested a probabilistic model for documents, whereby documents arise as a
convex combination of (i.e. distribution on) a small number of topic vectors,
each topic vector being a distribution on words (i.e. a vector of
word-frequencies). Similar models have since been used in a variety of
application areas; the Latent Dirichlet Allocation or LDA model of Blei et al.
is especially popular.
Theoretical studies of topic modeling focus on learning the model's
parameters assuming the data is actually generated from it. Existing approaches
for the most part rely on Singular Value Decomposition(SVD), and consequently
have one of two limitations: these works need to either assume that each
document contains only one topic, or else can only recover the span of the
topic vectors instead of the topic vectors themselves.
This paper formally justifies Nonnegative Matrix Factorization(NMF) as a main
tool in this context, which is an analog of SVD where all vectors are
nonnegative. Using this tool we give the first polynomial-time algorithm for
learning topic models without the above two limitations. The algorithm uses a
fairly mild assumption about the underlying topic matrix called separability,
which is usually found to hold in real-life data. A compelling feature of our
algorithm is that it generalizes to models that incorporate topic-topic
correlations, such as the Correlated Topic Model and the Pachinko Allocation
Model.
We hope that this paper will motivate further theoretical results that use
NMF as a replacement for SVD - just as NMF has come to replace SVD in many
applications
Factorized Topic Models
In this paper we present a modification to a latent topic model, which makes
the model exploit supervision to produce a factorized representation of the
observed data. The structured parameterization separately encodes variance that
is shared between classes from variance that is private to each class by the
introduction of a new prior over the topic space. The approach allows for a
more eff{}icient inference and provides an intuitive interpretation of the data
in terms of an informative signal together with structured noise. The
factorized representation is shown to enhance inference performance for image,
text, and video classification.Comment: ICLR 201
Learning Topic Models and Latent Bayesian Networks Under Expansion Constraints
Unsupervised estimation of latent variable models is a fundamental problem
central to numerous applications of machine learning and statistics. This work
presents a principled approach for estimating broad classes of such models,
including probabilistic topic models and latent linear Bayesian networks, using
only second-order observed moments. The sufficient conditions for
identifiability of these models are primarily based on weak expansion
constraints on the topic-word matrix, for topic models, and on the directed
acyclic graph, for Bayesian networks. Because no assumptions are made on the
distribution among the latent variables, the approach can handle arbitrary
correlations among the topics or latent factors. In addition, a tractable
learning method via optimization is proposed and studied in numerical
experiments.Comment: 38 pages, 6 figures, 2 tables, applications in topic models and
Bayesian networks are studied. Simulation section is adde
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