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We present an algorithm that computes exactly (optimally) the S-sparse (1≤S<D) maximum-L1-norm-projection principal component of a real-valued data matrix X ∈ ℝD×N that contains N samples of dimension D. For fixed sample support N, the optimal L1-sparse algorithm has linear complexity in data dimension, O(D). For fixed dimension D (thus, fixed sparsity S), the optimal L1-sparse algorithm has polynomial...
A linear-time algorithm termed SPARse Truncated Amplitude flow (SPARTA) is developed for the phase retrieval (PR) of sparse signals. Upon formulating the sparse PR as a non-convex empirical loss minimization task, SPARTA emerges as an iterative solver consisting of two components: s1) a sparse orthogonality-promoting initialization leveraging support recovery and principal component analysis; and,...
While speech quality and intelligibility prediction methods for normal-hearing and hearing-impaired listeners have found a lot of attention as a cost-saving complement to listening tests, analogous procedures for music signals are still rare. In this paper a method is proposed for predicting perceptual ratings of music as obtained by cochlear implant (CI) listeners. For this purpose a listening test...
Independent vector analysis (IVA) is an approach for joint blind source separation of several data sets that learns simultaneous unmixing transforms for each set. It assumes corresponding sources from different data sets to be statistically dependent. One of the main advantages is IVA's ability to retain subject-specific differences while simplifying comparison across subjects as the resulting components...
Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise...
We address the problem of determining the number of signals correlated between two high-dimensional data sets with small sample support. In this setting, conventional techniques based on canonical correlation analysis (CCA) cannot be directly applied since the canonical correlations are significantly overestimated when computed from few samples. To overcome this problem, a principal component analysis...
Recently, the advancement of face recognition technology, which manifests itself in the variety of applications such as ATM machines, CC cameras, personal identification, etc., brings about a new-fashioned surveillance situation to distinguish and identify any person. This paper tries to improve the face recognition process by introducing a new model, Face Clustering, in which the face angles are...
In this paper we exploit the use of the proposed RKPCA method ([1], [2], [3]) for sensor fault detection, localisation and reconstruction. To this end, a set of structured residues is generated by using partial RKPCA technique. Also to identify fault, the Reconstruction Based Contribution RBC approach [4] was used. The relevance of the evaluated techniques partial RKPCA and RBC is revealed on Continuous...
Timber Purchasing Managers' Index (PMI) has been perceived as the barometer of timber industry evolution. The weight scheme is at the core of constructing the index. In this study, an in-depth analysis on the weight scheme of Timber PMI is conducted. The empirical results reveal that the current weight does not affect the leading function of Timber PMI, but it is not the optimal weighting method....
In this paper, an integration of the independent component analysis (ICA) and an unsupervised feature selection scheme is proposed as a data engineering approach, called ICA-DEODA. The proposed feature extraction model enables beyond second order analysis which can powerfully underlie knowledge discovery methods. When applying the model for stock forecasting, firstly, independent components (ICs)...
This paper addresses the image retrieval problem of finding images in a large dataset that contain similar scenes or objects to a given query image. Often, this task is performed with the popular Bag-of-Words (BoW)-Model which quantizes local features such as SIFT for speeding up the retrieval by using an inverted file indexing scheme. We focus on the limits of the model for very large-scale datasets...
In this paper, we propose the novel remote sensing image classification algorithm based on the PCA and hidden Markov random field theory. Remote sensing image classification method based on the pattern recognition theory at home and abroad, many experts have done a lot of research work, machine learning has been in study of remote sensing image classification and information extraction has been widely...
The fast and on-site measurement of the real-time of seeds is of great importance for the farmers and scientists, but is difficult to achieve. At present, there are very few researches reported for this issue. Therefore, it is top urgent to develop an effective method of the determination of real-time of seeds. This study develops the spectral indices using the hyperspectral imaging technique for...
Feature extraction plays an important role in machinery fault diagnosis and prognosis. The features extracted from time, frequency and time-frequency domains are widely investigated to describe the properties of overall signal from different perspectives, seldom considering the sequential characteristic of time-series signal in which the fault information may be embedded. This paper investigates a...
Este artículo abarca un estudio del análisis hiperespectral en el proceso de fermentación de granos de cacao violeta. La aplicación de técnicas de procesamiento de imágenes hiperespectrales en el cacao es escasa. Este artículo presenta un estudio basado en el cálculo de índices espectrales, encontrando una correlación con los parámetros bioquímicos que indican una correcta evolución de la fermentación...
This work analyses consumption obtained from high-power appliances in order to determine it characteristic curve. Due it high consumption, induction cooker, washer and dryer were studied. Meter FLUKE 1735 was used to acquire a samples per second. Acquired data as: voltage, current, voltage and current harmonics. Principal components analysis was used to reduce the size of each load data matrix and...
Accurate wind power forecast is an important method for solving the utilization problem of new energy. Forecast evaluation results have been applied to the dynamic dispatch of power systems that utilize large-scale wind power. As the starting point in optimizing regional forecast evaluation, this study first gathered fully diverse power forecast evaluation indexes that are based on a traditional index...
With the recent success of visual features from deep convolutional neural networks (DCNN) in visual robot self-localization, it has become important and practical to address more general self-localization scenarios. In this paper, we address the scenario of self-localization from images with small overlap. We explicitly introduce a localization difficulty index as a decreasing function of view overlap...
A lab-made photoplethysmographic (PPG) device was applied to measure PPG signals of healthy cohorts and selected patients by medical doctors for arterial elasticity assessment. The off-line analysis on vascular health was done afterwards using a commercial software. The received information is repeatable and reproducible, however, sometimes not unambiguous. Temporal parameters of the signal components...
A new temporal sampling method for human action recognition using depth maps is proposed. The depth map frames are projected onto three orthogonal Cartesian planes respectively and the absolute difference between two successive projected ones is stacked to form the motion energy. Three sub-action frame sequences are selected accordingly, which constructs Adaptive Temporal Sampling (ATS) descriptor,...
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