The Expectation-Maximization (EM) algorithm is one of the most popular algorithms for parameter estimation from incomplete data, but its convergence can be slowfor some large-scale or complex problems. Extrapolation methods can effectively accelerate EM, but to ensure stability, the learning rate of extrapolation must be compromised. This paper describes the TJ2aEM method, a targeted extrapolation method that can extrapolate much more aggressively than competing methods without causing instability problems. We analyze its convergence properties and report experimental results.

It has been established that the second-order stochastic gradient descent (2SGD) method can potentially achieve generalization performance as well as empirical optimum in a single pass (i.e., epoch) through the training examples. However, 2SGD requires computing the inverse of the Hessian matrix of the loss function, which is prohibitively expensive. This paper presents Periodic Step-size Adaptation (PSA), which approximates the Jacobian matrix of the mapping function and explores a linear relation between the Jacobian and Hessian to approximate the Hessian periodically and achieve near-optimal results in experiments on a wide variety of models and tasks.

It has been established that the second-order stochastic gradient descent (SGD) method can potentially achieve generalization performance as well as empirical optimum in a single pass through the training examples. However, second-order SGD requires computing the inverse of the Hessian matrix of the loss function, which is prohibitively expensive for structured prediction problems that usually involve a very high dimensional feature space. This paper presents a new second-order SGD method, called Periodic Step-size Adaptation (PSA). PSA approximates the Jacobian matrix of the mapping function and explores a linear relation between the Jacobian and Hessian to approximate the Hessian, which is proved to be simpler and more effective than directly approximating Hessian in an on-line setting. We tested PSA on a wide variety of models and tasks, including large scale sequence labeling tasks using conditional random fields and large scale classification tasks using linear support vector machines and convolutional neural networks. Experimental results show that single-pass performance of PSA is always very close to empirical optimum.

MOTIVATION: Tagging gene and gene product mentions in scientific text is an important initial step of literature mining. In this article, we describe in detail our gene mention tagger participated in BioCreative 2 challenge and analyze what contributes to its good performance. Our tagger is based on the conditional random fields model (CRF), the most prevailing method for the gene mention tagging task in BioCreative 2. Our tagger is interesting because it accomplished the highest F-scores among CRF-based methods and second over all. Moreover, we obtained our results by mostly applying open source packages, making it easy to duplicate our results. RESULTS: We first describe in detail how we developed our CRF-based tagger. We designed a very high dimensional feature set that includes most of information that may be relevant. We trained bi-directional CRF models with the same set of features, one applies forward parsing and the other backward, and integrated two models based on the output scores and dictionary filtering. One of the most prominent factors that contributes to the good performance of our tagger is the integration of an additional backward parsing model. However, from the definition of CRF, it appears that a CRF model is symmetric and bi-directional parsing models will produce the same results. We show that due to different feature settings, a CRF model can be asymmetric and the feature setting for our tagger in BioCreative 2 not only produces different results but also gives backward parsing models slight but constant advantage over forward parsing model. To fully explore the potential of integrating bi-directional parsing models, we applied different asymmetric feature settings to generate many bi-directional parsing models and integrate them based on the output scores. Experimental results show that this integrated model can achieve even higher F-score solely based on the training corpus for gene mention tagging. AVAILABILITY: Data sets, programs and an on-line service of our gene mention tagger can be accessed at http://aiia.iis.sinica.edu.tw/biocreative2.htm.

For applications with consecutive incoming training examples, on-line learning has the potential to achieve a likelihood as high as off-line learning without scanning all available training examples and usually has a much smaller memory footprint. To train CRFson-line, this paper presents the Periodic Step size Adaptation (PSA) method to dynamically adjust the learning rates in stochastic gradient descent. We applied our method to three large scale text mining tasks. Experimental results show that PSA outperforms the best off-line algorithm, L-BFGS, by many hundred times, and outperforms the best on-line algorithm, SMD, by an order of magnitude in terms of the number of passes required to scan the training data set.

The expectation-maximization (EM) algorithm is one of the most popular algorithms for data mining from incomplete data. However, when applied to large data sets with a large proportion of missing data, the EM algorithm may converge slowly. The triple jump extrapolation method can effectively accelerate the EM algorithm by substantially reducing the number of iterations required for EM to converge. There are two options for the triple jump method, global extrapolation (TJEM) and componentwise extrapolation (CTJEM). We tried these two methods for a variety of probabilistic models and found that in general, global extraplolation yields a better performance, but there are cases where componentwise extrapolation yields very high speedup. In this paper, we investigate when componentwise extrapolation should be preferred. We conclude that, when the Jacobian of the EM mapping is diagonal or block diagonal, CTJEM should be preferred. We show how to determine whether a Jacobian is diagonal or block diagonal and experimentally confirm our claim. In particular, we show that CTJEM is especially effective for the semi-supervised Bayesian classifier model given a highly sparse data set.

This paper presents the triple jump framework for accelerating the EM algorithm and other bound optimization methods. The idea is to extrapolate the third search point based on the previous two search points found by regular EM. As the convergence rate of regular EM becomes slower, the distance of the triple jump is longer, and thus provide higher speedup for data sets where EM converges slowly. Experimental results show that the triple jump framework significantly outperforms EM and other acceleration methods of EM for a variety of probabilistic models, especially when the data set is sparse. The results also show that the triple jump framework is particularly effective for cluster models.

Predicting an individual customer's likelihood of purchasing a specific item forms the basis of many marketing activities, such as personalized shopping recommendation. Collaborative filtering and association rule mining can be applied to this problem, but in retail supermarkets, the problem becomes particularly challenging because of the sparsity and skewness of transaction data. We present HyPAM (hybrid Poisson aspect model), a new probabilistic graphical model that combines a Poisson mixture with a latent aspect class model to model customers' shopping behavior. We empirically compare HyPAM with two well-known recommenders, GroupLens (a correlation-based method), and IBM SmartPad (association rules and cosine similarity). Experimental results show that HyPAM outperforms the other recommenders by a large margin for two real-world retail supermarkets, ranking most of actual purchases in the top ten percent of the most likely purchased items. We also present a new visualization method, rank plot, to evaluate the quality of recommendations.

In this paper, firstly we discuss the architecture of a flexible automated production system under which all components are well-modularized. Then, a solution, EMFAK (event-driven multi-tasking flexible automation kernel) is proposed to speed up the construction of system control. Finally, a flexible assembly system using EMFAK is described to show how to apply it to a real flexible automated production system.