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Semi-Supervised Learning Literature Survey

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Applied Longitudinal Data Analysis Modeling Change And Event Occurrence

Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data is unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data is labeled.The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data is scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field.

Introduction to Semi-Supervised Learning

In many practical data mining applications such as text classification, unlabeled training examples are readily available, but labeled ones are fairly expensive to obtain. Therefore, semi supervised learning algorithms have aroused considerable interests from the data mining and machine learning fields. In recent years, graph-based semi supervised learning has been becoming one of the most active research areas in the semi supervised learning community. In this paper, a novel graph-based semi supervised learning approach is proposed based on a linear neighborhood model, which assumes that each data point can be linearly reconstructed from its neighborhood. Our algorithm, named linear neighborhood propagation (LNP), can propagate the labels from the labeled points to the whole data set using these linear neighborhoods with sufficient smoothness. A theoretical analysis of the properties of LNP is presented in this paper. Furthermore, we also derive an easy way to extend LNP to out-of-sample data. Promising experimental results are presented for synthetic data, digit, and text classification tasks.

Label Propagation through Linear Neighborhoods

Blink: The Power of Thinking without Thinking / Strangers to Ourselves: Discovering the Adaptive Unconscious

In this paper we present a new computational framework of affective-cognitive learning and decision making for affective agents, inspired by human learning and recent neuroscience and psychology. In the proposed framework ‘internal reward from cognition and emotion’ and ‘external reward from the external world’ serve as motivation in learning and decision making. We construct this model, integrating affect and cognition, with the aim of enabling machines to make smarter and more human-like decisions for better human-machine interactions.

/pdf/affective-cognitive-learning-and-decision-making-a-ncbw3d7r48.pdf

Affective-Cognitive learning and decision making: a motivational reward framework for affective agents

An online discussion recommendation system ranks and presents to users of a social media site discussions of interest preferably calculated through interest-matching. The discussion has many online posts. The system and method determine which post to present to the user as a sample snippet from such discussion using sentiment analysis. The sentiment of the snippet may be most polarized, sentiment matching the user's current mood, typical user sentiment, or others.

Computer system and method for sentiment-based recommendations of discussion topics in social media

A physically animated visual display is a robotic device, capable of multiple degree-of-freedom motion, that is adapted to improve a user's emotional state, cognitive performance, and comfort level through reactive and goal-directed manipulation of the position of the display. The affective-cognitive system comprises a feature extraction subsystem for deriving physical information about a user from data obtained from sensors, a perception subsystem for processing the data in order to determine the user's current emotional state, affective-cognitive state, or posture, an action selection subsystem for determining an action to be taken in response, and a motor system for physically animating the robotic device in accordance with the determined action. The system may include feedback modeling based on a reaction of the user to the movement of the apparatus increasing or decreasing the probability of choosing the current behavior based on the user's reaction.

Physically-animated Visual Display

The social Customer Relationship Management (CRM) landscape is attracting significant attention from customers and enterprises alike as a sustainable channel for tracking, managing and improving customer relations. Enterprises are taking a hard look at this open, unmediated platform because the community effect generated on this channel can have a telling effect on their brand image, potential market opportunity and customer loyalty. In this work we present our experiences in building a system that mines conversations on social platforms to identify and prioritize those posts and messages that are relevant to enterprises. The system presented in this work aims to empower an agent or a representative in an enterprise to monitor, track and respond to customer communication while also encouraging community participation.

A CRM system for social media: challenges and experiences

Regime switching vector autoregressive (RSVAR) models are typically used to model changing dependency structures of multivariate time series. These changing regimes are represented by using a first-order Markov process where the transition distribution reflects the probabilities of moving to one of the other regime in the subsequent time step. Instead of representing the state of the system at different points in time, we extend this framework by using an explicit time representation that allows us to query against probability distributions of when particular regime changes take place. In contrast to continuous time based approaches such as continuous time Bayesian networks or continuous time Markov processes, we do not rely on intensity matrices that describe trajectories of consecutive states. Here we define regime changes as events and understand time as context of an event. This allows us to integrate dependencies at different time granularities while being able to perform inference in a decomposed way. As a consequence, we can efficiently consider higher-order effects stretching across a large number of consecutive regimes. The underlying assumption is that timely evolution of variables between regime switches is completely captured by the VAR model or possibly a set of VAR models with varying measuring rates and that there is a representative set of multiple time series exhibiting similar higher-order regime dynamics. In this paper we show how such dynamics can be learned integrative with learning RSVAR model parameters and how the regime dynamics can be considered in the RSVAR inference procedures. We demonstrate the benefits of our approach based on a simple scenario. Further, an application to a typical prognostics scenario is presented, leading to the highest score in the IEEE PHM 2014 Data Challenge for the industrial track.

Switching vector autoregressive models with higher-order regime dynamics Application to prognostics and health management

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