Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Abstract: (2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.
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TL;DR: It is demonstrated that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence, especially in classification of evidence sentences.
Abstract: Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F1≈0.93, MCC≈0.74, iAUC≈0.99) and sentences (F1≈0.76, MCC≈0.65, iAUC≈0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence.
238 citations
Cites background from "Pattern Recognition and Machine Lea..."
...the bigram feature coefficients) that needed to be estimated from training data, which can potentially increase generalization error arising from increased model complexity [45]....
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07 Jun 2015
TL;DR: Based on the principles of PO-CR, a face alignment system that produces remarkably accurate results on the challenging iBUG data set outperforming previously proposed systems by a large margin.
Abstract: Cascaded regression approaches have been recently shown to achieve state-of-the-art performance for many computer vision tasks. Beyond its connection to boosting, cascaded regression has been interpreted as a learning-based approach to iterative optimization methods like the Newton's method. However, in prior work, the connection to optimization theory is limited only in learning a mapping from image features to problem parameters. In this paper, we consider the problem of facial deformable model fitting using cascaded regression and make the following contributions: (a) We propose regression to learn a sequence of averaged Jacobian and Hessian matrices from data, and from them descent directions in a fashion inspired by Gauss-Newton optimization. (b) We show that the optimization problem in hand has structure and devise a learning strategy for a cascaded regression approach that takes the problem structure into account. By doing so, the proposed method learns and employs a sequence of averaged Jacobians and descent directions in a subspace orthogonal to the facial appearance variation; hence, we call it Project-Out Cascaded Regression (PO-CR). (c) Based on the principles of PO-CR, we built a face alignment system that produces remarkably accurate results on the challenging iBUG data set outperforming previously proposed systems by a large margin. Code for our system is available from http://www.cs.nott.ac.uk/∼yzt/.
238 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...A plethora of regression methods have been employed to tackle the above mentioned problems including linear and ridge [4], Support Vector [31], Boosted [13], Gaussian process [26], and more recently, Deep Neural Nets [18]....
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TL;DR: Novel algorithms for total variation (TV) based blind deconvolution and parameter estimation utilizing a variational framework using a hierarchical Bayesian model to provide higher restoration performance than non-TV-based methods without any assumptions about the unknown hyperparameters.
Abstract: In this paper, we present novel algorithms for total variation (TV) based blind deconvolution and parameter estimation utilizing a variational framework. Using a hierarchical Bayesian model, the unknown image, blur, and hyperparameters for the image, blur, and noise priors are estimated simultaneously. A variational inference approach is utilized so that approximations of the posterior distributions of the unknowns are obtained, thus providing a measure of the uncertainty of the estimates. Experimental results demonstrate that the proposed approaches provide higher restoration performance than non-TV-based methods without any assumptions about the unknown hyperparameters.
237 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...Based on the above, we utilize the Gamma distribution for the hyperparameters , and , since it is the conjugate prior for the inverse variance (precision) of the Gaussian distribution....
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17 Jun 2007
TL;DR: Combining spatial and aspect models significantly improves the region-level classification accuracy, and models trained with image-level labels outperform PLSA trained with pixel-level ones.
Abstract: Considerable advances have been made in learning to recognize and localize visual object classes. Simple bag-of-feature approaches label each pixel or patch independently. More advanced models attempt to improve the coherence of the labellings by introducing some form of inter-patch coupling: traditional spatial models such as MRF's provide crisper local labellings by exploiting neighbourhood-level couplings, while aspect models such as PLSA and LDA use global relevance estimates (global mixing proportions for the classes appearing in the image) to shape the local choices. We point out that the two approaches are complementary, combining them to produce aspect-based spatial field models that outperform both approaches. We study two spatial models: one based on averaging over forests of minimal spanning trees linking neighboring image regions, the other on an efficient chain-based Expectation Propagation method for regular 8-neighbor Markov random fields. The models can be trained using either patch-level labels or image-level keywords. As input features they use factored observation models combining texture, color and position cues. Experimental results on the MSR Cambridge data sets show that combining spatial and aspect models significantly improves the region-level classification accuracy. In fact our models trained with image-level labels outperform PLSA trained with pixel-level ones.
237 citations
Cites background or methods from "Pattern Recognition and Machine Lea..."
...Each factor is a set of 1-D MRFs whose exact marginals are calculated using the Baum-Welch algorithm [3]....
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...PLSA can be viewed as a probabilistic generalization of PCA – a low-rank nonnegative approximation of the matrix of (empirical word probabilities for each document)× (documents) using factor matrices {p(w|t)}×{p(t|d)} obtained by Expectation-Maximization (EM) [3]....
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...For an introduction to the latter three methods see [3], and for more details on EP and its relation to loopy BP, variational and other approximations see [16]....
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...However there are various methods for approximating marginals, including (structured) Gibbs sampling [8], Variational Mean-Field (VMF), Loopy Belief Propagation (LBP), and Expectation Propagation (EP)....
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...When the node marginals are multi-modal, EP tends to smooth over the peaks and hence over-estimate the marginal entropies, but in practice its marginals are often found to be more accurate than those of VMF and LBP [3]....
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11 Aug 2013
TL;DR: Experimental results on two real-world datasets show that the proposed model is effective in discovering users' spatial-temporal topics, and outperforms state-of-the-art baselines significantly for the task of location prediction for tweets.
Abstract: Micro-blogging services, such as Twitter, and location-based social network applications have generated short text messages associated with geographic information, posting time, and user ids. The availability of such data received from users offers a good opportunity to study the user's spatial-temporal behavior and preference. In this paper, we propose a probabilistic model W4 (short for Who+Where+When+What) to exploit such data to discover individual users' mobility behaviors from spatial, temporal and activity aspects. To the best of our knowledge, our work offers the first solution to jointly model individual user's mobility behavior from the three aspects. Our model has a variety of applications, such as user profiling and location prediction; it can be employed to answer questions such as ``Can we infer the location of a user given a tweet posted by the user and the posting time?" Experimental results on two real-world datasets show that the proposed model is effective in discovering users' spatial-temporal topics, and outperforms state-of-the-art baselines significantly for the task of location prediction for tweets.
237 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...For this fully connected graph, we can re-order its nodes as follows [3]:...
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