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Surveys are used by hospitals to evaluate patient satisfaction and to improve operation. Collected satisfaction data is usually represented to the hospital administration using statistical charts and graphs. Although this statistical data and visualization is helpful, but because of the size and dimension of the dataset, it is very difficult if not impossible, to identify important factors that could...
EEG based vowel classification is currently gaining importance for its increasing applications in the next generation mind-driven type-writing. This paper addresses a novel approach to classify the mentally uttered alphabets in a specific three lettered format, where the first and the last letter represent two vowel sounds and the middle is a space, where no character is imagined. Such formatting...
The Liquid State Machine (LSM) exploits the computation capability of recurrent spiking neural networks by incorporating a randomly generated reservoir, which is often fixed. This standard choice relaxes the challenging need for training the complex recurrent reservoir. The fixed reservoir is used as a generic kernel to map the temporal input signals to the internal network dynamics, and a readout...
This paper develops a framework for determining the Remaining Useful Life (RUL) of aero-engines. The framework includes the following modular components: creating a moving time window, a suitable feature extraction method and a multi-layer neural network as the main machine learning algorithm. The proposed framework is evaluated on the publicly available C-MAPSS dataset. The prognostic accuracy of...
In this paper, we propose a novel and general framework for dimensionality reduction, called Relational Fisher Analysis (RFA). Unlike traditional dimensionality reduction methods, such as linear discriminant analysis (LDA) and marginal Fisher analysis (MFA), RFA seamlessly integrates relational information among data into the representation learning framework, which in general provides strong evidence...
Loop closure detection benefits simultaneous localization and mapping (SLAM) in building a consistent map of the environment by reducing the accumulate error. Handcrafted features have been successfully used in traditional approaches, whereas in this paper, we show that unsupervised features extracted by deep learning models, can improves the accuracy of loop closure detection. In particular, we employ...
We propose an effective subspace selection scheme as a post-processing step to improve results obtained by sparse subspace clustering (SSC). Our method starts by the computation of stable subspaces using a novel random sampling scheme. Thus constructed preliminary subspaces are used to identify the initially incorrectly clustered data points and then to reassign them to more suitable clusters based...
The Johnson-Lindenstrauss (JL) lemma, with known probability, sets a lower bound q0 on the dimension for which a random projection of p-dimensional vector data is guaranteed to be within (1±ε) of being an isometry in a randomly projected downspace. We study several ways to identify a “good” rogue random projection when the target downspace has dimensions below the JL limit. The tools used towards...
This paper presents an improvement of the ELMVIS+ method that is proposed for fast nonlinear dimensionality reduction. The ELMVIS++C has an additional supervised learning component compared to ELMVIS+, which is originally an unsupervised method as like the majority of the other dimensionality reduction method. This component prevents samples under the same class being separated apart from each other...
Wind energy is an important component in the renewable energy mix, but successful integration into existing grid infrastructure is a major challenge. In this context, the accurate prediction of future wind generation power is extremely valuable as it would facilitate more efficient and sustainable provision. In a previous work, we proposed a method for wind power prediction based on an ensemble of...
For big, high-dimensional dense features, it is important to learn compact binary codes or compress them for greater memory efficiency. This paper proposes a Binarized Multilinear PCA (BMP) method for this problem with Free-Form Reshaping (FFR) of such features to higher-order tensors, lifting the structure-modelling restriction in traditional tensor models. The reshaped tensors are transformed to...
Traditional nonnegative matrix factorization (NMF) is an unsupervised method for linear feature extraction. Recently, NMF with block strategy is shown to be able to extract more sparse and discriminative information of the images. To enhance the discriminative power of NMF, this paper proposes a block kernel nonnegative matrix factorization (BKNMF) based on the kernel theory and block technique. Kernel...
In this paper, we present a new and effective dimensionality reduction method called locality sparsity preserving projections (LSPP). Locality preserving projections (LPP) and sparsity preserving projections (SPP) only focus on an aspect of local structure and sparse reconstructive information of the dataset, respectively. The proposed method integrates the sparse reconstructive information and local...
Kernel Spectral Clustering (KSC) solves a weighted kernel principal component analysis problem in a primal-dual optimization framework. It builds an unsupervised model on a small subset of data using the dual solution of the optimization problem. This allows KSC to have a powerful out-of-sample extension property leading to good cluster generalization w.r.t. unseen data points. However, in the presence...
Feature extraction is an essential preprocessing step in machine learning and data mining. Generally, supervised feature extraction algorithms with prior knowledge outperform unsupervised ones without prior knowledge. In particular, nearly all existing supervised feature extraction algorithms employ class labels or pairwise constraints as supervised information. In this paper, we propose to employ...
As the emergement of high-throughput measurement technologies, we are entering the big data era. Modern data are often generated from heterogeneous multiple sources, thus can be called multi-view data. The challenge of effectively integrating such data for decision making and novel knowledge discovery is raised. Matrix factorization methods have historically played important roles in various analyses...
This paper discusses feature extraction methods. The feature extraction methods such principal component analysis and multiple discriminant analysis are very important techniques in machine learning research areas. The characteristic of feature extraction is to transform the data from a difficultly classified space to a easily classified space. There are many conventional machine learning methods...
A new non-parametric method for reducing the number of dimensions in binary and continuous data, and for measuring the complexity of binary and continuous datasets, is introduced. The method, named Structural Manifold Analysis (SMA), is based on “Generalized Invariance Structure Theory” [1–6], a theory that has been successful in characterizing and accurately predicting human concept learning and...
Participation of class-wise noisy patterns may mislead the selection process of relevant patterns for subspace projection. And modelling between-class scatter for each class using the patterns that are nearer to the corresponding class decision boundary may improve the quality of feature generation. In this manuscript, a novel dimensionality reduction method, named Maximum Class Boundary Criterion...
Random forests are a class of ensemble methods for classification and regression with randomizing mechanism in bagging instances and selecting feature subspace. For high dimensional data, the performance of random forests degenerates because of the random sampling feature subspace for each node in the construction of decision trees. To address the issue, in this paper, we propose a new Principal Component...
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