151,966 research outputs found
Memory-Efficient Topic Modeling
As one of the simplest probabilistic topic modeling techniques, latent
Dirichlet allocation (LDA) has found many important applications in text
mining, computer vision and computational biology. Recent training algorithms
for LDA can be interpreted within a unified message passing framework. However,
message passing requires storing previous messages with a large amount of
memory space, increasing linearly with the number of documents or the number of
topics. Therefore, the high memory usage is often a major problem for topic
modeling of massive corpora containing a large number of topics. To reduce the
space complexity, we propose a novel algorithm without storing previous
messages for training LDA: tiny belief propagation (TBP). The basic idea of TBP
relates the message passing algorithms with the non-negative matrix
factorization (NMF) algorithms, which absorb the message updating into the
message passing process, and thus avoid storing previous messages. Experimental
results on four large data sets confirm that TBP performs comparably well or
even better than current state-of-the-art training algorithms for LDA but with
a much less memory consumption. TBP can do topic modeling when massive corpora
cannot fit in the computer memory, for example, extracting thematic topics from
7 GB PUBMED corpora on a common desktop computer with 2GB memory.Comment: 20 pages, 7 figure
Online Tensor Methods for Learning Latent Variable Models
We introduce an online tensor decomposition based approach for two latent
variable modeling problems namely, (1) community detection, in which we learn
the latent communities that the social actors in social networks belong to, and
(2) topic modeling, in which we infer hidden topics of text articles. We
consider decomposition of moment tensors using stochastic gradient descent. We
conduct optimization of multilinear operations in SGD and avoid directly
forming the tensors, to save computational and storage costs. We present
optimized algorithm in two platforms. Our GPU-based implementation exploits the
parallelism of SIMD architectures to allow for maximum speed-up by a careful
optimization of storage and data transfer, whereas our CPU-based implementation
uses efficient sparse matrix computations and is suitable for large sparse
datasets. For the community detection problem, we demonstrate accuracy and
computational efficiency on Facebook, Yelp and DBLP datasets, and for the topic
modeling problem, we also demonstrate good performance on the New York Times
dataset. We compare our results to the state-of-the-art algorithms such as the
variational method, and report a gain of accuracy and a gain of several orders
of magnitude in the execution time.Comment: JMLR 201
A New Approach to Speeding Up Topic Modeling
Latent Dirichlet allocation (LDA) is a widely-used probabilistic topic
modeling paradigm, and recently finds many applications in computer vision and
computational biology. In this paper, we propose a fast and accurate batch
algorithm, active belief propagation (ABP), for training LDA. Usually batch LDA
algorithms require repeated scanning of the entire corpus and searching the
complete topic space. To process massive corpora having a large number of
topics, the training iteration of batch LDA algorithms is often inefficient and
time-consuming. To accelerate the training speed, ABP actively scans the subset
of corpus and searches the subset of topic space for topic modeling, therefore
saves enormous training time in each iteration. To ensure accuracy, ABP selects
only those documents and topics that contribute to the largest residuals within
the residual belief propagation (RBP) framework. On four real-world corpora,
ABP performs around to times faster than state-of-the-art batch LDA
algorithms with a comparable topic modeling accuracy.Comment: 14 pages, 12 figure
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