Clustering by non-negative matrix factorization with independent principal component initialization

Non negative matrix factorization (NMF) is a dimensionality reduction and clustering method, and has been applied to many areas such as bioinformatics, face images classification, and so on. Based on the traditional NMF, researchers recently have put forward several new algorithms on the initialization area to improve its performance. In this paper, we explore the clustering performance of the NMF algorithm, with emphasis on the initialization problem. We propose an initialization method based on independent principal component analysis (IPCA) for NMF. The experiments were carried out on the four real datasets and the results showed that the IPCA-based initialization of NMF gets better clustering of the datasets compared with both random and PCA-based initializations

An enhanced initialization method for non-negative matrix factorization

Non-negative matrix factorization (NMF) is a dimensionality reduction tool, and has been applied to many areas such as bioinformatics, face image classification, etc. However, it often converges to some local optima because of its random initial NMF factors (W and H matrices). To solve this problem, some researchers have paid much attention to the NMF initialization problem. In this paper, we first apply the k-means clustering to initialize the factor W, and then we calculate the initial factor H using four different initialization methods (three standard and one new). The experiments were carried out on the eight real datasets and the results showed that the proposed method (EIn-NMF) achieved less error and faster convergence compared with both random initialization based NMF and the three standard methods for k-means based NMF

Clustering analysis for gene expression data: a methodological review

Clustering is one of most useful tools for the microarray gene expression data analysis. Although there have been many reviews and surveys in the literature, many good and effective clustering ideas have not been collected in a systematic way for some reasons. In this paper, we review five clustering families representing five clustering concepts rather than five algorithms. We also review some clustering validations and collect a list of benchmark gene expression datasets

Evolutionary nonnegative matrix factorization for data compression

This paper aims at improving non-negative matrix factor- ization (NMF) to facilitate data compression. An evolutionary updat- ing strategy is proposed to solve the NMF problem iteratively based on three sets of updating rules including multiplicative, firefly and sur- vival of the fittest rules. For data compression application, the quality of the factorized matrices can be evaluated by measurements such as spar- sity, orthogonality and factorization error to assess compression quality in terms of storage space consumption, redundancy in data matrix and data approximation accuracy. Thus, the fitness score function that drives the evolving procedure is designed as a composite score that takes into account all these measurements. A hybrid initialization scheme is per- formed to improve the rate of convergence, allowing multiple initial can- didates generated by different types of NMF initialization approaches. Effectiveness of the proposed method is demonstrated using Yale and ORL image datasets

Nonnegative matrix analysis for data clustering and compression

Nonnegative matrix factorization (NMF) has becoming an increasingly popular data processing tool these years, widely used by various communities including computer vision, text mining and bioinformatics. It is able to approximate each data sample in a data collection by a linear combination of a set of nonnegative basis vectors weighted by nonnegative weights. This often enables meaningful interpretation of the data, motivates useful insights and facilitates tasks such as data compression, clustering and classification. These subsequently lead to various active roles of NMF in data analysis, e.g., dimensionality reduction tool [11, 75], clustering tool[94, 82, 13, 39], feature engine [40], source separation tool [38], etc. Different methods based on NMF are proposed in this thesis: The modification of k- means clustering is chosen as one of the initialisation methods for NMF. Experimental results demonstrate the excellence of this method with improved compression performance. Independent principal component analysis (IPCA) which combines the advantage of both principal component analysis (PCA) and independent component analysis (ICA) has been chosen as the significant initialisation method for NMF with improved clustering accuracy. We have proposed the new evolutionary optimization strategy for NMF driven by three proposed update schemes in the solution space, saying NMF rule (or original movement), firefly rule (or beta movement) and survival of the fittest rule (or best movement). This proposed update strategy facilitates both the clustering and compression problems by using the different system objective functions that make use of the clustering and compression quality measurements. A hybrid initialisation approach is used by including the state-of-the-art NMF initialization methods as seed knowledge to increase the rate of convergence. There is no limitation for the number and the type of the initialization methods used for the proposed optimisation approach. Numerous computer experiments using the benchmark datasets verify the theoretical results, make comparisons among the techniques in measures of clustering/compression accuracy. Experimental results demonstrate the excellence of these methods with im- proved clustering/compression performance. In the application of EEG dataset, we employed several standard algorithms to provide clustering on preprocessed EEG data. We also explored ensemble clustering to obtain some tight clusters. We can make some statements based on the results we have got: firstly, normalization is necessary for this EEG brain dataset to obtain reasonable clustering; secondly, k-means, k-medoids and HC-Ward provide relatively better clustering results; thirdly, ensemble clustering enables us to tune the tightness of the clusters so that the research can be focused

Regression analysis for paths inference in a novel Proton CT system

In this work, we analyse the proton paths inference for the construction of CT imagery based on a new proton CT proton system, which can record multiple proton paths/residual energies. Based on the recorded paths of multiple protons, every proton path is inferred. The inferred proton paths can then be used for the residual energies detection and CT imagery construction for analyzing a specific tissue. Different regression methods (linear regression and Gaussian process regression models) are exploited for the path inference of every proton in this work. The studies on a recorded proton trajectories dataset show that the Gaussian process regression method achieves better accuracies for the path inference, from both path assignment accuracy and root mean square errors (RMSEs) studies

A new Gaussian mixture method with exactly exploiting the negative information for GMTI radar tracking in a low-observable environment

This paper investigates the problem of ground vehicle tracking with a Ground Moving Target Indicator (GMTI) radar. In practice, the movement of ground vehicles may involve several different manoeuvring types (acceleration, deceleration, standstill, etc.). Consequently, the GMTI radar may lose measurements when the radial velocity of the ground vehicle is below a threshold when it stops, i.e. falling into the Doppler blind region. Besides, there will be false alarms in low-observable environments where there exist high noises interferences. In this paper, we develop a novel algorithm for the GMTI tracking in a low-observable environment with false alarms while exactly incorporating the ‘negative information’ (i.e., the target is likely to stop when no measurements are recorded) based on the Bayesian inference framework. For the Bayesian inference implementation, the Gaussian mixture approximation method is adopted to approximate related distributions, while different filtering algorithms (including both extended Kalman filter and its generalization for interval-censored measurements) are applied for updating the Gaussian mixture components. Target state estimation can be directly obtained through the Gaussian mixture model for the GMTI tracking at every time instance. We have compared the developed method with other state-of-the-art ones and the simulation results show that the proposed method substantially outperforms the existing methods for the GMTI tracking problem

Deep learning based prediction on greenhouse crop yield combined TCN and RNN

Funding: This research was supported as part of SMARTGREEN, an Interreg project supported by the North Sea Programme of the European Regional Development Fund of the European Union.Peer reviewedPublisher PD

Multiple model ballistic missile tracking with state-dependent transitions and Gaussian particle filtering

This paper proposes a new method for tracking the entire trajectory of a ballistic missile from launch to impact on the ground. Multiple state models are used to represent the different ballistic missile dynamics in three flight phases: boost, coast and reentry. In particular, the transition probabilities between state models are represented in a state-dependent way by utilising domain knowledge. Based on this modelling system and radar measurements, a state-dependent interacting multiple model approach based on Gaussian particle filtering is developed to accurately estimate information describing the ballistic missile such as the phase of flight, position, velocity and relevant missile parameters. Comprehensive numerical simulation studies show that the proposed method outperforms the traditional multiple model approaches for ballistic missile tracking

Enhancing Privacy with Optical Element Design for Fall Detection

Falling poses significant risks, especially for the geriatric population. In this study, we introduce an innovative approach to privacy-preserving fall detection using computer vision. Our technique leverages a deep neural network (DNN) to accurately identify falling events in input images, while simultaneously prioritizing privacy through the implementation of an optical element. The experimental results establish that our proposed method outperforms alternative hardware and software-based privacy-preserving approaches in terms of encryption level and accuracy. These results are derived from an extensive dataset encompassing diverse falling scenarios