Application of Non-negative Matrix Factorization to LC/MS data

International audienceLiquid Chromatography-Mass Spectrometry (LC/MS) provides large datasets from which one needs to extract the relevant information. Since these data are made of non-negative mixtures of non-negative mass spectra, non-negative matrix factorization (NMF) is well suited for its processing, but it has barely been used in LC/MS. Also, these data are very difficult to deal with since they are usually contaminated with non-Gaussian noise and the intensities vary on several orders of magnitude. In this article, we show the feasibility of the NMF approach on these data. We also propose an adaptation of one of the algorithms aiming at specifically dealing with LC/MS data. We finally perform experiments and compare standard NMF algorithms on both simulated data and an annotated LC/MS dataset. This lets us evaluate the influence of the noise model and the data model on the recovery of the sources

Fast Robust PCA on Graphs

Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the curse of dimensionality. However, often such methods are accompanied with three different problems: high computational complexity (usually associated with the nuclear norm minimization), non-convexity (for matrix factorization methods) and susceptibility to gross corruptions in the data. In this paper we propose a principal component analysis (PCA) based solution that overcomes these three issues and approximates a low-rank recovery method for high dimensional datasets. We target the low-rank recovery by enforcing two types of graph smoothness assumptions, one on the data samples and the other on the features by designing a convex optimization problem. The resulting algorithm is fast, efficient and scalable for huge datasets with O(nlog(n)) computational complexity in the number of data samples. It is also robust to gross corruptions in the dataset as well as to the model parameters. Clustering experiments on 7 benchmark datasets with different types of corruptions and background separation experiments on 3 video datasets show that our proposed model outperforms 10 state-of-the-art dimensionality reduction models. Our theoretical analysis proves that the proposed model is able to recover approximate low-rank representations with a bounded error for clusterable data

Audio source separation for music in low-latency and high-latency scenarios

Aquesta tesi proposa mètodes per tractar les limitacions de les tècniques existents de separació de fonts musicals en condicions de baixa i alta latència. En primer lloc, ens centrem en els mètodes amb un baix cost computacional i baixa latència. Proposem l'ús de la regularització de Tikhonov com a mètode de descomposició de l'espectre en el context de baixa latència. El comparem amb les tècniques existents en tasques d'estimació i seguiment dels tons, que són passos crucials en molts mètodes de separació. A continuació utilitzem i avaluem el mètode de descomposició de l'espectre en tasques de separació de veu cantada, baix i percussió. En segon lloc, proposem diversos mètodes d'alta latència que milloren la separació de la veu cantada, gràcies al modelatge de components específics, com la respiració i les consonants. Finalment, explorem l'ús de correlacions temporals i anotacions manuals per millorar la separació dels instruments de percussió i dels senyals musicals polifònics complexes.Esta tesis propone métodos para tratar las limitaciones de las técnicas existentes de separación de fuentes musicales en condiciones de baja y alta latencia. En primer lugar, nos centramos en los métodos con un bajo coste computacional y baja latencia. Proponemos el uso de la regularización de Tikhonov como método de descomposición del espectro en el contexto de baja latencia. Lo comparamos con las técnicas existentes en tareas de estimación y seguimiento de los tonos, que son pasos cruciales en muchos métodos de separación. A continuación utilizamos y evaluamos el método de descomposición del espectro en tareas de separación de voz cantada, bajo y percusión. En segundo lugar, proponemos varios métodos de alta latencia que mejoran la separación de la voz cantada, gracias al modelado de componentes que a menudo no se toman en cuenta, como la respiración y las consonantes. Finalmente, exploramos el uso de correlaciones temporales y anotaciones manuales para mejorar la separación de los instrumentos de percusión y señales musicales polifónicas complejas.This thesis proposes specific methods to address the limitations of current music source separation methods in low-latency and high-latency scenarios. First, we focus on methods with low computational cost and low latency. We propose the use of Tikhonov regularization as a method for spectrum decomposition in the low-latency context. We compare it to existing techniques in pitch estimation and tracking tasks, crucial steps in many separation methods. We then use the proposed spectrum decomposition method in low-latency separation tasks targeting singing voice, bass and drums. Second, we propose several high-latency methods that improve the separation of singing voice by modeling components that are often not accounted for, such as breathiness and consonants. Finally, we explore using temporal correlations and human annotations to enhance the separation of drums and complex polyphonic music signals

Learning Ideological Latent space in Twitter

People are shifting from traditional news sources to online news at an incredibly fast rate. However, the technology behind online news consumption forces users to be confined to content that confirms with their own point of view. This has led to social phenomena like polarization of point-of-view and intolerance towards opposing views. In this thesis we study information filter bubbles from a mathematical standpoint. We use data mining techniques to learn a liberal-conservative ideology space in Twitter and presents a case study on how such a latent space can be used to tackle the filter bubble problem on social networks. We model the problem of learning liberal-conservative ideology as a constrained optimization problem. Using matrix factorization we uncover an ideological latent space for content consumption and social interaction habits of users in Twitter. We validate our model on real world Twitter dataset on three controversial topics - "Obamacare", "gun control" and "abortion". Using the proposed technique we are able to separate users by their ideology with 95% purity. Our analysis shows that there is a very high correlation (0.8 - 0.9) between the estimated ideology using machine learning and true ideology collected from various sources. Finally, we re-examine the learnt latent space, and present a case study showcasing how this ideological latent space can be used to develop exploratory and interactive interfaces that can help in diffusing the information filter bubble. Our matrix factorization based model for learning ideology latent space, along with the case studies provide a theoretically solid as well as a practical and interesting point-of-view to online polarization. Further, it provides a strong foundation and suggests several avenues for future work in multiple emerging interdisciplinary research areas, for instance, humanly interpretable and explanatory machine learning, transparent recommendations and a new field that we coin as Next Generation Social Networks

Robust and Adversarial Data Mining

In the domain of data mining and machine learning, researchers have made significant contributions in developing algorithms handling clustering and classification problems. We develop algorithms under assumptions that are not met by previous works. (i) In adversarial learning, which is the study of machine learning techniques deployed in non-benign environments. We design an algorithm to show how a classifier should be designed to be robust against sparse adversarial attacks. Our main insight is that sparse feature attacks are best defended by designing classifiers which use L1 regularizers. (ii) The different properties between L1 (Lasso) and L2 (Tikhonov or Ridge) regularization has been studied extensively. However, given a data set, principle to follow in terms of choosing the suitable regularizer is yet to be developed. We use mathematical properties of the two regularization methods followed by detailed experimentation to understand their impact based on four characteristics. (iii) The identification of anomalies is an inherent component of knowledge discovery. In lots of cases, the number of features of a data set can be traced to a much smaller set of features. We claim that algorithms applied in a latent space are more robust. This can lead to more accurate results, and potentially provide a natural medium to explain and describe outliers. (iv) We also apply data mining techniques on health care industry. In a lot cases, health insurance companies cover unnecessary costs carried out by healthcare providers. The potential adversarial behaviours of surgeon physicians are addressed. We describe a specific con- text of private healthcare in Australia and describe our social network based approach (applied to health insurance claims) to understand the nature of collaboration among doctors treating hospital inpatients and explore the impact of collaboration on cost and quality of care. (v) We further develop models that predict the behaviours of orthopaedic surgeons in regard to surgery type and use of prosthetic device. An important feature of these models is that they can not only predict the behaviours of surgeons but also provide explanation for the predictions

Machine and deep learning meet genome-scale metabolic modeling

Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process