Biclustering on expression data: A review

Biclustering has become a popular technique for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. Most of biclustering approaches use a measure or cost function that determines the quality of biclusters. In such cases, the development of both a suitable heuristics and a good measure for guiding the search are essential for discovering interesting biclusters in an expression matrix. Nevertheless, not all existing biclustering approaches base their search on evaluation measures for biclusters. There exists a diverse set of biclustering tools that follow different strategies and algorithmic concepts which guide the search towards meaningful results. In this paper we present a extensive survey of biclustering approaches, classifying them into two categories according to whether or not use evaluation metrics within the search method: biclustering algorithms based on evaluation measures and non metric-based biclustering algorithms. In both cases, they have been classified according to the type of meta-heuristics which they are based on.Ministerio de Economía y Competitividad TIN2011-2895

Learning Possibilistic Logic Theories

Vi tar opp problemet med å lære tolkbare maskinlæringsmodeller fra usikker og manglende informasjon. Vi utvikler først en ny dyplæringsarkitektur, RIDDLE: Rule InDuction with Deep LEarning (regelinduksjon med dyp læring), basert på egenskapene til mulighetsteori. Med eksperimentelle resultater og sammenligning med FURIA, en eksisterende moderne metode for regelinduksjon, er RIDDLE en lovende regelinduksjonsalgoritme for å finne regler fra data. Deretter undersøker vi læringsoppgaven formelt ved å identifisere regler med konfidensgrad knyttet til dem i exact learning-modellen. Vi definerer formelt teoretiske rammer og viser forhold som må holde for å garantere at en læringsalgoritme vil identifisere reglene som holder i et domene. Til slutt utvikler vi en algoritme som lærer regler med tilhørende konfidensverdier i exact learning-modellen. Vi foreslår også en teknikk for å simulere spørringer i exact learning-modellen fra data. Eksperimenter viser oppmuntrende resultater for å lære et sett med regler som tilnærmer reglene som er kodet i data.We address the problem of learning interpretable machine learning models from uncertain and missing information. We first develop a novel deep learning architecture, named RIDDLE (Rule InDuction with Deep LEarning), based on properties of possibility theory. With experimental results and comparison with FURIA, a state of the art method, RIDDLE is a promising rule induction algorithm for finding rules from data. We then formally investigate the learning task of identifying rules with confidence degree associated to them in the exact learning model. We formally define theoretical frameworks and show conditions that must hold to guarantee that a learning algorithm will identify the rules that hold in a domain. Finally, we develop an algorithm that learns rules with associated confidence values in the exact learning model. We also propose a technique to simulate queries in the exact learning model from data. Experiments show encouraging results to learn a set of rules that approximate rules encoded in data.Doktorgradsavhandlin

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensin

Context dependent spectral unmixing.

A hyperspectral unmixing algorithm that finds multiple sets of endmembers is proposed. The algorithm, called Context Dependent Spectral Unmixing (CDSU), is a local approach that adapts the unmixing to different regions of the spectral space. It is based on a novel function that combines context identification and unmixing. This joint objective function models contexts as compact clusters and uses the linear mixing model as the basis for unmixing. Several variations of the CDSU, that provide additional desirable features, are also proposed. First, the Context Dependent Spectral unmixing using the Mahalanobis Distance (CDSUM) offers the advantage of identifying non-spherical clusters in the high dimensional spectral space. Second, the Cluster and Proportion Constrained Multi-Model Unmixing (CC-MMU and PC-MMU) algorithms use partial supervision information, in the form of cluster or proportion constraints, to guide the search process and narrow the space of possible solutions. The supervision information could be provided by an expert, generated by analyzing the consensus of multiple unmixing algorithms, or extracted from co-located data from a different sensor. Third, the Robust Context Dependent Spectral Unmixing (RCDSU) introduces possibilistic memberships into the objective function to reduce the effect of noise and outliers in the data. Finally, the Unsupervised Robust Context Dependent Spectral Unmixing (U-RCDSU) algorithm learns the optimal number of contexts in an unsupervised way. The performance of each algorithm is evaluated using synthetic and real data. We show that the proposed methods can identify meaningful and coherent contexts, and appropriate endmembers within each context. The second main contribution of this thesis is consensus unmixing. This approach exploits the diversity and similarity of the large number of existing unmixing algorithms to identify an accurate and consistent set of endmembers in the data. We run multiple unmixing algorithms using different parameters, and combine the resulting unmixing ensemble using consensus analysis. The extracted endmembers will be the ones that have a consensus among the multiple runs. The third main contribution consists of developing subpixel target detectors that rely on the proposed CDSU algorithms to adapt target detection algorithms to different contexts. A local detection statistic is computed for each context and then all scores are combined to yield a final detection score. The context dependent unmixing provides a better background description and limits target leakage, which are two essential properties for target detection algorithms

Temporal decision making using unsupervised learning

With the explosion of ubiquitous continuous sensing, on-line streaming clustering continues to attract attention. The requirements are that the streaming clustering algorithm recognize and adapt clusters as the data evolves, that anomalies are detected, and that new clusters are automatically formed as incoming data dictate. In this dissertation, we develop a streaming clustering algorithm, MU Streaming Clustering (MUSC), that is based on coupling a Gaussian mixture model (GMM) with possibilistic clustering to build an adaptive system for analyzing streaming multi-dimensional activity feature vectors. For this reason, the possibilistic C-Means (PCM) and Automatic Merging Possibilistic Clustering Method (AMPCM) are combined together to cluster the initial data points, detect anomalies and initialize the GMM. MUSC achieves our goals when tested on synthetic and real-life datasets. We also compare MUSC's performance with Sequential k-means (sk-means), Basic Sequential Clustering Algorithm (BSAS), and Modified BSAS (MBSAS) here MUSC shows superiority in the performance and accuracy. The performance of a streaming clustering algorithm needs to be monitored over time to understand the behavior of the streaming data in terms of new emerging clusters and number of outlier data points. Incremental internal Validity Indices (iCVIs) are used to monitor the performance of an on-line clustering algorithm. We study the internal incremental Davies-Bouldin (DB), Xie-Beni (XB), and Dunn internal cluster validity indices in the context of streaming data analysis. We extend the original incremental DB (iDB) to a more general version parameterized by the exponent of membership weights. Then we illustrate how the iDB can be used to analyze and understand the performance of MUSC algorithm. We give examples that illustrate the appearance of a new cluster, the effect of different cluster sizes, handling of outlier data samples, and the effect of the input order on the resultant cluster history. In addition, we investigate the internal incremental Davies-Bouldin (iDB) cluster validity index in the context of big streaming data analysis. We analyze the effect of large numbers of samples on the values of the iCVI (iDB). We also develop online versions of two modified generalized Dunn's indices that can be used for dynamic evaluation of evolving (cluster) structure in streaming data. We argue that this method is a good way to monitor the ongoing performance of online clustering algorithms and we illustrate several types of inferences that can be drawn from such indices. We compare the two new indices to the incremental Xie-Beni and Davies-Bouldin indices, which to our knowledge offer the only comparable approach, with numerical examples on a variety of synthetic and real data sets. We also study the performance of MUSC and iCVIs with big streaming data applications. We show the advantage of iCVIs in monitoring large streaming datasets and in providing useful information about the data stream in terms of emergence of a new structure, amount of outlier data, size of the clusters, and order of data samples in each cluster. We also propose a way to project streaming data into a lower space for cases where the distance measure does not perform as expected in the high dimensional space. Another example of streaming is the data acivity data coming from TigerPlace and other elderly residents' apartments in and around Columbia. MO. TigerPlace is an eldercare facility that promotes aging-in-place in Columbia Missouri. Eldercare monitoring using non-wearable sensors is a candidate solution for improving care and reducing costs. Abnormal sensor patterns produced by certain resident behaviors could be linked to early signs of illness. We propose an unsupervised method for detecting abnormal behavior patterns based on a new context preserving representation of daily activities. A preliminary analysis of the method was conducted on data collected in TigerPlace. Sensor firings of each day are converted into sequences of daily activities. Then, building a histogram from the daily sequences of a resident, we generate a single data vector representing that day. Using the proposed method, a day with hundreds of sequences is converted into a single data point representing that day and preserving the context of the daily routine at the same time. We obtained an average Area Under the Curve (AUC) of 0.9 in detecting days where elder adults need to be assessed. Our approach outperforms other approaches on the same datset. Using the context preserving representation, we develoed a multi-dimensional alert system to improve the existing single-dimensional alert system in TigerPlace. Also, this represenation is used to develop a framework that utilizes sensor sequence similarity and medical concepts extracted from the EHR to automatically inform the nursing staff when health problems are detected. Our context preserving representation of daily activities is used to measure the similarity between the sensor sequences of different days. The medical concepts are extracted from the nursing notes using MetamapLite, an NLP tool included in the Unified Medical Language System (UMLS). The proposed idea is validated on two pilot datasets from twelve Tiger Place residents, with a total of 5810 sensor days out of which 1966 had nursing notes

Advances in transfer learning methods based on computational intelligence

Traditional machine learning and data mining have made tremendous progress in many knowledge-based areas, such as clustering, classification, and regression. However, the primary assumption in all of these areas is that the training and testing data should be in the same domain and have the same distribution. This assumption is difficult to achieve in real-world applications due to the limited availability of labeled data. Associated data in different domains can be used to expand the availability of prior knowledge about future target data. In recent years, transfer learning has been used to address such cross-domain learning problems by using information from data in a related domain and transferring that data to the target task. The transfer learning methodology is utilized in this work with unsupervised and supervised learning methods. For unsupervised learning, a novel transfer-learning possibilistic c-means (TLPCM) algorithm is proposed to handle the PCM clustering problem in a domain that has insufficient data. Moreover, TLPCM overcomes the problem of differing numbers of clusters between the source and target domains. The proposed algorithm employs the historical cluster centers of the source data as a reference to guide the clustering of the target data. The experimental studies presented here were thoroughly evaluated, and they demonstrate the advantages of TLPCM in both synthetic and real-world transfer datasets. For supervised learning, a transfer learning (TL) technique is used to pre-train a CNN model on posture data and then fine-tune it on the sleep stage data. We used a ballistocardiography (BCG) bed sensor to collect both posture and sleep stage data to provide a non-invasive, in-home monitoring system that tracks changes in the subjects' health over time. The quality of sleep has a significant impact on health and life. This study adopts a hierarchical and none-hierarchical classification structure to develop an automatic sleep stage classification system using ballistocardiogram (BCG) signals. A leave-one-subject-out cross-validation (LOSO-CV) procedure is used for testing classification performance in most of the experiments. Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Deep Neural Networks DNNs are complementary in their modeling capabilities, while CNNs have the advantage of reducing frequency variations, LSTMs are good at temporal modeling. Polysomnography (PSG) data from a sleep lab was used as the ground truth for sleep stages, with the emphasis on three sleep stages, specifically, awake, rapid eye movement (REM), and non-REM sleep (NREM). Moreover, a transfer learning approach is employed with supervised learning to address the cross-resident training problem to predict early illness. We validate our method by conducting a retrospective study on three residents from TigerPlace, a retirement community in Columbia, MO, where apartments are fitted with wireless networks of motion and bed sensors. Predicting the early signs of illness in older adults by using a continuous, unobtrusive nursing home monitoring system has been shown to increase the quality of life and decrease care costs. Illness prediction is based on sensor data and uses algorithms such as support vector machine (SVM) and k-nearest neighbors (kNN). One of the most significant challenges related to the development of prediction algorithms for sensor networks is the use of knowledge from previous residents to predict new ones' behaviors. Each day, the presence or absence of illness was manually evaluated using nursing visit reports from a homegrown electronic medical record (EMR) system. In this work, the transfer learning SVM approach outperformed three other methods, i.e., regular SVM, one-class SVM, and one-class kNN.Includes bibliographical references (pages 114-127)