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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06 Nov 2011
TL;DR: An extension of bag-of-words image representations to encode spatial layout using the Fisher kernel framework and a Gaussian mixture model is introduced, which yields an image representation that is computationally efficient, compact, and yields excellent performance while using linear classifiers.
Abstract: We introduce an extension of bag-of-words image representations to encode spatial layout. Using the Fisher kernel framework we derive a representation that encodes the spatial mean and the variance of image regions associated with visual words. We extend this representation by using a Gaussian mixture model to encode spatial layout, and show that this model is related to a soft-assign version of the spatial pyramid representation. We also combine our representation of spatial layout with the use of Fisher kernels to encode the appearance of local features. Through an extensive experimental evaluation, we show that our representation yields state-of-the-art image categorization results, while being more compact than spatial pyramid representations. In particular, using Fisher kernels to encode both appearance and spatial layout results in an image representation that is computationally efficient, compact, and yields excellent performance while using linear classifiers.
197 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...Therefore it is common to use a diagonal approximation of F ; where [17] uses an analytical approximation, we follow [2] (section 6....
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...The spatial model of each visual word can be learned using the EM algorithm [2] from the patch locations associated with each visual word....
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TL;DR: The BrainAGE framework indicates discrepancies in brain aging and could thus serve as an indicator for cognitive func- tioning in the future.
Abstract: We recently proposed a novel method that aggregates the multidimensional aging pattern across the brain to a single value. This method proved to provide stable and reliable estimates of brain aging - even across different scanners. While investigating longi- tudinal changes in BrainAGE in about 400 elderly subjects, we discovered that patients with Alzheimer's disease and subjects who had converted to AD within 3 years showed accelerated brain atrophy by +6 years at baseline. An additional increase in BrainAGE accumulated to a score of about +9 years during follow-up. Accelerated brain aging was related to prospective cognitive decline and disease severity. In conclusion, the BrainAGE framework indicates discrepancies in brain aging and could thus serve as an indicator for cognitive func- tioning in the future.
197 citations
Cites background from "Pattern Recognition and Machine Lea..."
...More details can be found in Bishop (2006), Schölkopf and Smola (2002), and Tipping (2000)....
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TL;DR: MyBehavior represents the first attempt to create personalized, contextualized, actionable suggestions automatically from self-tracked information and lessons learned about the difficulty of manual logging and usability concerns, as well as future directions, are discussed.
Abstract: Background: A dramatic rise in health-tracking apps for mobile phones has occurred recently. Rich user interfaces make manual logging of users’ behaviors easier and more pleasant, and sensors make tracking effortless. To date, however, feedback technologies have been limited to providing overall statistics, attractive visualization of tracked data, or simple tailoring based on age, gender, and overall calorie or activity information. There are a lack of systems that can perform automated translation of behavioral data into specific actionable suggestions that promote healthier lifestyle without any human involvement. Objective: MyBehavior, a mobile phone app, was designed to process tracked physical activity and eating behavior data in order to provide personalized, actionable, low-effort suggestions that are contextualized to the user’s environment and previous behavior. This study investigated the technical feasibility of implementing an automated feedback system, the impact of the suggestions on user physical activity and eating behavior, and user perceptions of the automatically generated suggestions. Methods: MyBehavior was designed to (1) use a combination of automatic and manual logging to track physical activity (eg, walking, running, gym), user location, and food, (2) automatically analyze activity and food logs to identify frequent and nonfrequent behaviors, and (3) use a standard machine-learning, decision-making algorithm, called multi-armed bandit (MAB), to generate personalized suggestions that ask users to either continue, avoid, or make small changes to existing behaviors to help users reach behavioral goals.
We enrolled 17 participants, all motivated to self-monitor and improve their fitness, in a pilot study of MyBehavior. In a randomized two-group trial, investigators randomly assigned participants to receive either MyBehavior’s personalized suggestions (n=9) or nonpersonalized suggestions (n=8), created by professionals, from a mobile phone app over 3 weeks. Daily activity level and dietary intake was monitored from logged data. At the end of the study, an in-person survey was conducted that asked users to subjectively rate their intention to follow MyBehavior suggestions. Results: In qualitative daily diary, interview, and survey data, users reported MyBehavior suggestions to be highly actionable and stated that they intended to follow the suggestions. MyBehavior users walked significantly more than the control group over the 3 weeks of the study (P=.05). Although some MyBehavior users chose lower-calorie foods, the between-group difference was not significant (P=.15). In a poststudy survey, users rated MyBehavior’s personalized suggestions more positively than the nonpersonalized, generic suggestions created by professionals (P<.001). Conclusions: MyBehavior is a simple-to-use mobile phone app with preliminary evidence of efficacy. To the best of our knowledge, MyBehavior represents the first attempt to create personalized, contextualized, actionable suggestions automatically from self-tracked information (ie, manual food logging and automatic tracking of activity). Lessons learned about the difficulty of manual logging and usability concerns, as well as future directions, are discussed. Trial Registration: ClinicalTrials.gov NCT02359981; https://clinicaltrials.gov/ct2/show/NCT02359981 (Archived by WebCite at http://www.webcitation.org/6YCeoN8nv).
197 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...A number of statistical features (eg, mean, variance, zero-crossing rate) are extracted from the sensor data and a machine-learning model—Gaussian Mixture Model (GMM) [22]—is applied to map the extracted feature values into the four most common daily physical activities—walking, running, stationary (sitting or standing), and driving....
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...A number of statistical features (eg, mean, variance, zero-crossing rate) are extracted from the sensor data and a machine-learning model—Gaussian Mixture Model (GMM) [22]—is applied to map the extracted feature values into the four most common daily physical activities—walking, running, stationary (sitting or standing), and driving....
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...CDC: Centers for Disease Control and Prevention FBM: Fogg Behavior Model GMM: Gaussian Mixture Model GPS: Global Positioning System MAB: multi-armed bandit METS: Metabolic Equivalents of Task q25: lower quartile q50: median q75: upper quartile RCT: randomized control trial USDA: United States Department of Agriculture WHO: World Health Organization JMIR mHealth uHealth 2015 | vol. 3 | iss....
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TL;DR: Multi-view representation learning has become a rapidly growing direction in machine learning and data mining areas as mentioned in this paper, and a comprehensive survey of multi-view representations can be found in this paper.
Abstract: Recently, multi-view representation learning has become a rapidly growing direction in machine learning and data mining areas. This paper introduces two categories for multi-view representation learning: multi-view representation alignment and multi-view representation fusion. Consequently, we first review the representative methods and theories of multi-view representation learning based on the perspective of alignment, such as correlation-based alignment. Representative examples are canonical correlation analysis (CCA) and its several extensions. Then from the perspective of representation fusion we investigate the advancement of multi-view representation learning that ranges from generative methods including multi-modal topic learning, multi-view sparse coding, and multi-view latent space Markov networks, to neural network-based methods including multi-modal autoencoders, multi-view convolutional neural networks, and multi-modal recurrent neural networks. Further, we also investigate several important applications of multi-view representation learning. Overall, this survey aims to provide an insightful overview of theoretical foundation and state-of-the-art developments in the field of multi-view representation learning and to help researchers find the most appropriate tools for particular applications.
196 citations
TL;DR: An algorithm to decrease the size of the training set for kNN regression(DISKR) by firstly removing the outlier instances that impact the performance of regressor, and then sorts the left instances by the difference on output among instances and their nearest neighbors.
196 citations
Cites background from "Pattern Recognition and Machine Lea..."
...Supervised learning infers a function(learner) from a training data T , which is a collection of training examples called samples [1]....
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