Causes of fish depletion: a factor analysis approach

A study was conducted in 54 wetlands of 13 districts of Assam, India to evaluate the causes of fish depletion. Twenty-two variables were considered for the study. Seven factors were extracted through factor analysis (Principal Component Analysis) based on Eigen Value Criteria of more than one. These seven factors together accounted for 69.3% of the total variance. Based on the characteristics of the variables, all the factors were given descriptive names. These variables can be used to measure the extent of management deficiency of the causes of fish depletion in the wetlands. The factors are management deficiency, organic load interference, catchment condition, extrinsic influence, fishermen’s ignorance, external environment and aquaculture program. Management deficiency accounted for a substantial portion of the total variance.

Optimization on the orthogonal group for independent component analysis

This paper derives a new algorithm that performs independent component analysis (ICA) by optimizing the contrast function of the RADICAL algorithm. The core idea of the proposed optimization method is to combine the global search of a good initial condition with a gradient-descent algorithm. This new ICA algorithm performs faster than the RADICAL algorithm (based on Jacobi rotations) while still preserving, and even enhancing, the strong robustness properties that result from its contrast. © Springer-Verlag Berlin Heidelberg 2007.

Geometric optimization methods for independent component analysis applied on gene expression data

DNA microarrays provide a huge amount of data and require therefore dimensionality reduction methods to extract meaningful biological information. Independent Component Analysis (ICA) was proposed by several authors as an interesting means. Unfortunately, experimental data are usually of poor quality- because of noise, outliers and lack of samples. Robustness to these hurdles will thus be a key feature for an ICA algorithm. This paper identifies a robust contrast function and proposes a new ICA algorithm. © 2007 IEEE.

On Combining Morphological Component Analysis and Concentric Morphology Model for Mammographic Mass Detection

Mammographic mass detection is an important task for the early diagnosis of breast cancer. However, it is difficult to distinguish masses from normal regions because of their abundant morphological characteristics and ambiguous margins. To improve the mass detection performance, it is essential to effectively preprocess mammogram to preserve both the intensity distribution and morphological characteristics of regions. In this paper, morphological component analysis is first introduced to decompose a mammogram into a piecewise-smooth component and a texture component. The former is utilized in our detection scheme as it effectively suppresses both structural noises and effects of blood vessels. Then, we propose two novel concentric layer criteria to detect different types of suspicious regions in a mammogram. The combination is evaluated based on the Digital Database for Screening Mammography, where 100 malignant cases and 50 benign cases are utilized. The sensitivity of the proposed scheme is 99% in malignant, 88% in benign, and 95.3% in all types of cases. The results show that the proposed detection scheme achieves satisfactory detection performance and preferable compromises between sensitivity and false positive rates.

Structural Analysis of Network Traffic Flows

Network traffic arises from the superposition of Origin-Destination (OD) flows. Hence, a thorough understanding of OD flows is essential for modeling network traffic, and for addressing a wide variety of problems including traffic engineering, traffic matrix estimation, capacity planning, forecasting and anomaly detection. However, to date, OD flows have not been closely studied, and there is very little known about their properties. We present the first analysis of complete sets of OD flow timeseries, taken from two different backbone networks (Abilene and Sprint-Europe). Using Principal Component Analysis (PCA), we find that the set of OD flows has small intrinsic dimension. In fact, even in a network with over a hundred OD flows, these flows can be accurately modeled in time using a small number (10 or less) of independent components or dimensions. We also show how to use PCA to systematically decompose the structure of OD flow timeseries into three main constituents: common periodic trends, short-lived bursts, and noise. We provide insight into how the various constituents contribute to the overall structure of OD flows and explore the extent to which this decomposition varies over time.