Scalable Data Mining via Constrained Low Rank Approximation

Matrix and tensor approximation methods are recognised as foundational tools for modern data analytics. Their strength lies in their long history of rigorous and principled theoretical foundations, judicious formulations via various constraints, along with the availability of fast computer programs. Multiple Constrained Low Rank Approximation (CLRA) formulations exist for various commonly encountered tasks like clustering, dimensionality reduction, anomaly detection, amongst others. The primary challenge in modern data analytics is the sheer volume of data to be analysed, often requiring multiple machines to just hold the dataset in memory. This dissertation presents CLRA as a key enabler of scalable data mining in distributed-memory parallel machines. Nonnegative Matrix Factorisation (NMF) is the primary CLRA method studied in this dissertation. NMF imposes nonnegativity constraints on the factor matrices and is a well studied formulation known for its simplicity, interpretability, and clustering prowess. The major bottleneck in most NMF algorithms is a distributed matrix-multiplication kernel. We develop the Parallel Low rank Approximation with Nonnegativity Constraints (PLANC) software package, building on the earlier MPI-FAUN library, which includes an efficient matrix-multiplication kernel tailored to the CLRA case. It employs carefully designed parallel algorithms and data distributions to avoid unnecessary computation and communication. We extend PLANC to include several optimised Nonnegative Least-Squares (NLS) solvers and symmetric constraints, effectively employing the optimised matrix-multiplication kernel. We develop a parallel inexact Gauss-Newton algorithm for Symmetric Nonnegative Matrix Factorisation (SymNMF). In particular PLANC is able to efficiently utilise second-order information when imposing symmetry constraints without incurring the prohibitive memory and computational costs associated with these methods. We are able to observe 70% efficiency while scaling up these methods. We develop new parallel algorithms for fusing and analysing data with multiple modalities in the Joint Nonnegative Matrix Factorisation (JointNMF) context. JointNMF is capable of knowledge discovery when both feature-data and data-data information is present in a data source. We extend PLANC to handle this case of simultaneously approximating two different large input matrices and study the various trade-offs encountered in the bottleneck matrix-multiplication kernel. We show that these ideas translate naturally to the multilinear setting when data is presented in the form of a tensor. A bottleneck computation analogous to the matrix-multiply, the Matricised-Tensor Times Khatri-Rao Product (MTTKRP) kernel, is implemented. We conclude by describing some avenues for future research which extend the work and ideas in this dissertation. In particular, we consider the notion of structured sparsity, where the user has some control over the nonzero pattern, which appears in computations for various tasks like cross-validation, working with missing values, robust CLRA models, and in the semi-supervised setting.Ph.D

Iodine nutrition and its assessment during pregnancy: a need for concern

Micro nutrient iodine was essential in biosynthesis of thyroid hormones (TH). These hormones play a vital role in the growth and development of human beings at various stages of life. TH are required for normal brain development at embryonic, fetal and at past natal stages. If deficiency of iodine occurs then thyroid hormone production was impaired, if deficiency of TH occurs it can cause irreversible damage of brain which leads to various disorders like metal retarded ness, cretinism and goiter known as iodine deficiency disorders (IDD). When compared to normal adults, pregnant women and school children 5-12 years age are more vulnerable IDD. Due to the physiological variations during pregnancy iodine requirements rose significantly from 150 µg/day in non-pregnant adult women to 250 μg/day. However recent iodine nutrition studies showed adequate iodine status but few countries like Sweden and South Africa showed iodine deficiency. This review gives an over view on iodine nutrition on global front

A Baseline for the Multivariate Comparison of Resting-State Networks

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease

A Novel Approach for Edge Detection using Modified ACIES Filtering

Abstract — The most important humiliating attribute of an image is noise, which may become obvious during image capturing, communication or processing and may conceivably be reliant on or sovereign of image. In order to supplement the high- frequency components in this paper we are proposing a new class of filter called ACIES filter which implies spatial filter shape that has a high positive component at the centre. Since restraint of noise can only be achieved by smoothing the image. Sharpening with ACIES filter highlights the fine details of an image and enhances the clarity of the image at the boundaries. Experimental results show that the concert of the proposed ACIES filter is acceptable and Quality of the consequent images is remarkably well even under the strongly noise-corrupted conditions. If the edges in an image can be recognized specifically, then all the objects in the image can be located and basic properties such as region, edge, and contour can be measured. The performance of the proposed approach is measured with image quality metrics such a

Schizophrenia Shows Disrupted Links between Brain Volume and Dynamic Functional Connectivity

Studies featuring multimodal neuroimaging data fusion for understanding brain function and structure, or disease characterization, leverage the partial information available in each of the modalities to reveal data variations not exhibited through the independent analyses. Similar to other complex syndromes, the characteristic brain abnormalities in schizophrenia may be better understood with the help of the additional information conveyed by leveraging an advanced modeling method involving multiple modalities. In this study, we propose a novel framework to fuse feature spaces corresponding to functional magnetic resonance imaging (functional) and gray matter (structural) data from 151 schizophrenia patients and 163 healthy controls. In particular, the features for the functional and structural modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) maps and the intensities of the gray matter (GM) maps, respectively. The dFNC maps are estimated from group independent component analysis (ICA) network time-courses by first computing windowed functional correlations using a sliding window approach, and then estimating subject specific states from this windowed data using temporal ICA followed by spatio-temporal regression. For each subject, the functional data features are horizontally concatenated with the corresponding GM features to form a combined feature space that is subsequently decomposed through a symmetric multimodal fusion approach involving a combination of multiset canonical correlation analysis (mCCA) and joint ICA (jICA). Our novel combined analyses successfully linked changes in the two modalities and revealed significantly disrupted links between GM volumes and time-varying functional connectivity in schizophrenia. Consistent with prior research, we found significant group differences in GM comprising regions in the superior parietal lobule, precuneus, postcentral gyrus, medial/superior frontal gyrus, superior/middle temporal gyrus, insula and fusiform gyrus, and several significant aberrations in the inter-regional functional connectivity strength as well. Importantly, structural and dFNC measures have independently shown changes associated with schizophrenia, and in this work we begin the process of evaluating the links between the two, which could shed light on the illness beyond what we can learn from a single imaging modality. In future work, we plan to evaluate replication of the inferred structure-function relationships in independent partitions of larger multi-modal schizophrenia datasets