Unsupervised Shift-invariant Feature Learning from Time-series Data

Unsupervised feature learning is one of the key components of machine learningand articial intelligence. Learning features from high dimensional streaming data isan important and dicult problem which is incorporated with number of challenges.Moreover, feature learning algorithms need to be evaluated and generalized for timeseries with dierent patterns and components. A detailed study is needed to clarifywhen simple algorithms fail to learn features and whether we need more complicatedmethods.In this thesis, we show that the systematic way to learn meaningful featuresfrom time-series is by using convolutional or shift-invariant versions of unsupervisedfeature learning. We experimentally compare the shift-invariant versions of clustering,sparse coding and non-negative matrix factorization algorithms for: reconstruction,noise separation, prediction, classication and simulating auditory lters from acousticsignals. The results show that the most ecient and highly scalable clustering algorithmwith a simple modication in inference and learning phase is able to produce meaningfulresults. Clustering features are also comparable with sparse coding and non-negativematrix factorization in most of the tasks (e.g. classication) and even more successful insome (e.g. prediction). Shift invariant sparse coding is also used on a novel application,inferring hearing loss from speech signal and produced promising results.Performance of algorithms with regard to some important factors such as: timeseries components, number of features and size of receptive eld is also analyzed. Theresults show that there is a signicant positive correlation between performance of clusteringwith degree of trend, frequency skewness, frequency kurtosis and serial correlationof data, whereas, the correlation is negative in the case of dataset average bandwidth.Performance of shift invariant sparse coding is aected by frequency skewness, frequencykurtosis and serial correlation of data. Non-Negative matrix factorization is influenced by data characteristics same as clustering

Active Perception by Interaction with Other Agents in a Predictive Coding Framework: Application to Internet of Things Environment

Predicting the state of an agent\u27s partially-observable environment is a problem of interest in many domains. Typically in the real world, the environment consists of multiple agents, not necessarily working towards a common goal. Though the goal and sensory observation for each agent is unique, one agent might have acquired some knowledge that may benefit the other. In essence, the knowledge base regarding the environment is distributed among the agents. An agent can sample this distributed knowledge base by communicating with other agents. Since an agent is not storing the entire knowledge base, its model can be small and its inference can be efficient and fault-tolerant. However, the agent needs to learn -- when, with whom and what -- to communicate (in general interact) under different situations.This dissertation presents an agent model that actively and selectively communicates with other agents to predict the state of its environment efficiently. Communication is a challenge when the internal models of other agents is unknown and unobservable. The proposed agent learns communication policies as mappings from its belief state to when, with whom and what to communicate. The policies are learned using predictive coding in an online manner, without any reinforcement. The proposed agent model is evaluated on widely-studied applications, such as human activity recognition from multimodal, multisource and heterogeneous sensor data, and transferring knowledge across sensor networks. In the applications, either each sensor or each sensor network is assumed to be monitored by an agent. The recognition accuracy on benchmark datasets is comparable to the state-of-the-art, even though our model has significantly fewer parameters and infers the state in a localized manner. The learned policy reduces number of communications. The agent is tolerant to communication failures and can recognize the reliability of each agent from its communication messages. To the best of our knowledge, this is the first work on learning communication policies by an agent for predicting the state of its environment

Synthesizing Skeletal Motion and Physiological Signals as a Function of a Virtual Human's Actions and Emotions

Round-the-clock monitoring of human behavior and emotions is required in many healthcare applications which is very expensive but can be automated using machine learning (ML) and sensor technologies. Unfortunately, the lack of infrastructure for collection and sharing of such data is a bottleneck for ML research applied to healthcare. Our goal is to circumvent this bottleneck by simulating a human body in virtual environment. This will allow generation of potentially infinite amounts of shareable data from an individual as a function of his actions, interactions and emotions in a care facility or at home, with no risk of confidentiality breach or privacy invasion. In this paper, we develop for the first time a system consisting of computational models for synchronously synthesizing skeletal motion, electrocardiogram, blood pressure, respiration, and skin conductance signals as a function of an open-ended set of actions and emotions. Our experimental evaluations, involving user studies, benchmark datasets and comparison to findings in the literature, show that our models can generate skeletal motion and physiological signals with high fidelity. The proposed framework is modular and allows the flexibility to experiment with different models. In addition to facilitating ML research for round-the-clock monitoring at a reduced cost, the proposed framework will allow reusability of code and data, and may be used as a training tool for ML practitioners and healthcare professionals

Multiple heads outsmart one: A computational model for distributed decision making

Distributed cognition and decision making has been a topic of intense research in the recent years. In this paper, a computational model of distributed decision making using a community of predictive coding agents is developed. The agents are embodied multimodal entities and situated in a shared environment. They have different visibility of the environment due to unique sensory and generative models. We show that communication between agents helps each of them reach a shared decision in a way that cannot be reached by brain processes in a single agent. Using a simulated environment, we show that sensory limitations may lead to incorrect or delayed causal inferences giving rise to conflicts in the mind of a predictive coding agent, and communication helps to resolve such conflicts and overcome the limitations

Epoc: Efficient perception via optimal communication

We propose an agent model capable of actively and selectively communicating with other agents to predict its environmental state efficiently. Selecting whom to communicate with is a challenge when the internal model of other agents is unobservable. Our agent learns a communication policy as a mapping from its belief state to with whom to communicate in an online and unsupervised manner, without any reinforcement. Human activity recognition from multimodal, multisource and heterogeneous sensor data is used as a testbed to evaluate the proposed model where each sensor is assumed to be monitored by an agent. The recognition accuracy on benchmark datasets is comparable to the state-of-the-art even though our model uses significantly fewer parameters and infers the state in a localized manner. The learned policy reduces number of communications. The agent is tolerant to communication failures and can recognize unreliable agents through their communication messages. To the best of our knowledge, this is the first work on learning communication policies by an agent for predicting its environmental state

Unsupervised Feature Learning From Time-Series Data Using Linear Models

In the Internet of Things (IoT), heterogenous sensors generate time-series data with different properties. The problem of unsupervised feature learning from a time-series dataset poses two challenges. First, it is known that centroids obtained by clustering time-series with high overlap do not reflect their patterns, i.e., subsequence time-series clustering is meaningless. In this paper, we show that principal component analysis, sparse coding, and non-negative matrix factorization are also meaningless for the same task, and that the systematic approach to learning meaningful features from time-series is by using the shift-invariant versions of these algorithms. Second, by comparing their shift-invariant versions on different kinds of time-series for reconstruction, prediction and classification, we show that no one algorithm is best suited for all time-series. This comparison leads to a method for automatically selecting the suitable feature learning algorithm for a given time-series dataset based on its structural properties. Generality of the method and significance of the structural properties are examined using statistical tests. The method can be implemented as a simple logic circuit, convenient for embedding in IoT hardware

Learning Communication Policies for Knowledge Transfer between Agents

We present an agent model in the predictive coding framework that selectively communicates with other agents to predictthe state of its environment efficiently. Selective communication is a challenge when the internal models of other agentsare unknown and unobservable. Communication helps agents to transfer the knowledge they have acquired in differentsituations. Recognition of daily activities of individuals living in different homes served as a testbed for evaluating themodel. Two publicly-available datasets, collected from unique homes, are used. Behavioral patterns of individuals in thosehomes are also unique. Each home is assumed to be monitored by an agent. We experimentally show that the agents cantransfer knowledge by communicating the most informative messages. The messages are interpretable. The agents learnpatterns of daily activities for any individual, and communicate using a vocabulary of words. Our model is more accuratethan traditional transfer learning models for the same task

Unsupervised Feature Learning from Time-Series Data Using Linear Models

In the Internet of Things (IoT), heterogenous sensors generate time-series data with different properties. The problem of unsupervised feature learning from a time-series dataset poses two challenges. First, it is known that centroids obtained by clustering time-series with high overlap do not reflect their patterns, i.e., subsequence time-series clustering is meaningless. In this paper, we show that principal component analysis, sparse coding, and non-negative matrix factorization are also meaningless for the same task, and that the systematic approach to learning meaningful features from time-series is by using the shift-invariant versions of these algorithms. Second, by comparing their shift-invariant versions on different kinds of time-series for reconstruction, prediction and classification, we show that no one algorithm is best suited for all time-series. This comparison leads to a method for automatically selecting the suitable feature learning algorithm for a given time-series dataset based on its structural properties. Generality of the method and significance of the structural properties are examined using statistical tests. The method can be implemented as a simple logic circuit, convenient for embedding in IoT hardware

Multiple heads outsmart one: A computational modelfor distributed decision making

Distributed cognition and decision making has been a topic ofintense research in the recent years. In this paper, a computa-tional model of distributed decision making using a commu-nity of predictive coding agents is developed. The agents areembodied multimodal entities and situated in a shared envi-ronment. They have different visibility of the environment dueto unique sensory and generative models. We show that com-munication between agents helps each of them reach a shareddecision in a way that cannot be reached by brain processes ina single agent. Using a simulated environment, we show thatsensory limitations may lead to incorrect or delayed causal in-ferences giving rise to conflicts in the mind of a predictive cod-ing agent, and communication helps to resolve such conflictsand overcome the limitations

Synthesizing skeletal motion and physiological signals as a function of a virtual human’s actions and emotions

Round-the-clock monitoring of human behavior and emotions is required in many healthcare applications which is very expensive but can be automated using machine learning (ML) and sensor technologies. Unfortunately, the lack of infrastructure for collection and sharing of such data is a bottleneck for ML research applied to healthcare. Our goal is to circumvent this bottleneck by simulating a human body in virtual environment. This will allow generation of potentially infinite amounts of shareable data from an individual as a function of his actions, interactions and emotions in a care facility or at home, with no risk of confidentiality breach or privacy invasion. In this paper, we develop for the first time a system consisting of computational models for synchronously synthesizing skeletal motion, electrocardiogram, blood pressure, respiration, and skin conductance signals as a function of an open-ended set of actions and emotions. Our experimental evaluations, involving user studies, benchmark datasets and comparison to findings in the literature, show that our models can generate skeletal motion and physiological signals with high fidelity. The proposed framework is modular and allows the flexibility to experiment with different models. In addition to facilitating ML research for round-the-clock monitoring at a reduced cost, the proposed framework will allow reusability of code and data, and may be used as a training tool for ML practitioners and healthcare professionals