The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Sentiment Analysis tools, developed for analyzing social media text or product reviews, work poorly on a Software Engineering (SE) dataset. Since prior studies have found developers expressing sentiments during various SE activities, there is a need for a customized sentiment analysis tool for the SE domain. On this goal, we manually labeled 2000 review comments to build a training dataset and used...
In traditional text sentiment analysis methods, text feature vector has the problem of high dimensionality and high sparseness. In view of this situation, we can cluster the similar words together and use the generated clusters to fit into a new dimension so that the text feature vector dimension will be decreased. By using Word2Vec tool and K-means clustering algorithm, this task can be completed...
Compressed sensing is a signal acquisition scheme that measures signals at sub-Nyquist rate amenable to sparse recovery, with high probability, from a reduced set of measurements. One of the main requirements of compressive sensing is the sparsity of the class of signals of interest in some basis. A method to construct a sparsifying basis for a class of signals using information theoretic measures...
This paper presents a combination of machine learning and lexicon-based approaches for sentiment analysis of students feedback. The textual feedback, typically collected towards the end of a semester, provides useful insights into the overall teaching quality and suggests valuable ways for improving teaching methodology. The paper describes a sentiment analysis model trained using TF-IDF and lexicon-based...
Sentiment analysis is a process of identifying and categorizing opinions expressed in a piece of text. It classifies the text into positive, negative or neutral. Lexicon-based and Supervised Machine Learning-based are the two main approaches in sentiment analysis. Bag-of-words model is used to represent the text as a vector of independent words and machine learning algorithms are used for classification...
Extreme machine learning and its variants have shown good generalization performance and high leaning speed in many applications through its fast convergence. Despite the parallel and distributed ELM on MapReduce framework able to handle very large scale dataset for bigdata applications, the process of coping up with the rapidly updating data is a challenging one. Among the unified algorithms, the...
Sparse representation is a novel methodology that has off late received substantial attention for image classification and recognition. This paper presents a PCA-based dictionary building for sparse recognition. Recursive least square based auto-associative neural network model has been used for principal component extraction. Suggested network structure supports data compression along with principal...
The P300 event-related potential is often used as input signal for BCI control. BCI researchers often invest time in studies on stimulation and classification procedures that remain below results already achieved. Translational studies are sparse and also their results have only little impact on BCI research and development. Potential reasons for the lack of substantial translational research including...
Credit risk analysis seeks to determine whether a customer is likely to default on the financial obligation, which is a very important problem in finance. In this paper, we will present a machine learning framework to deal with this problem by formulating it as a binary classification problem. The framework consists of two parts: dictionary learning and classifier training. Firstly, we introduce a...
In this paper, three new algorithms are presented by applying group idea and collaborative thought to projective dictionary pair learning (DPL). These algorithms further extend the framework of discriminative dictionary learning (DL). Based on projective dictionary pair learning which realizes the goals of signal representation and pattern classification by learning a synthesis dictionary and an analysis...
We analyze the effects of device variability during experimental image reconstruction using memristor crossbar arrays. The effects of device variability during online and offline training were carefully studied, along with device failures including SA0 and SA1. SA1 failure was found to significantly affect image reconstruction results, and a practical approach was developed to mitigate the effects...
The liver shapes are complex, pathological changes severely affect the liver shapes. In order to realize the segmentation of the boundary of liver in CT images, the liver shapes dictionary is built, the input CT images are sparse represented by the angular points in gold standard liver boundary dictionary, and the best matches is selected to be the final segmentation result. Experimental results show...
In order to solve the problem of time-consuming dictionary training process, we propose an image super-resolution reconstruction approach based on fusion of K-SVD algorithm and semi-coupled dictionary learning framework. In this paper, we use K-SVD algorithm to training the dictionary pair in the semi-coupled dictionary learning model. In comparison with the existing methods, experiment results on...
As a fundamental and effective method, sparse representation based classification (SRC) has been applied to computer vision field for many years. However, SRC assumes that the training samples in each class contribute equally to the dictionary which will cause high residual errors and instability. In order to solve the problem and improve classification performance further, class specific centralized...
Recently, dictionary learning based sparse representation algorithm has been widely adopted and achieved satisfying performance in image classification. However, sparse representation based classification (SRC) as well as collaborative representation based classification (CRC) always result in high residual error due to their basic assumption that considers training samples as dictionary directly...
In analysis dictionary learning, the learned dictionary may contain similar atoms, leading to a degenerate dictionary. To address this problem, we propose a novel incoherent analysis dictionary learning algorithm with the ℓ1-norm for sparsity and simultaneously with the coherence penalty. The whole problem is convex but nonsmooth due to the sparsity regularizer and the coherence penalty. Hence, the...
in today's world, everyone is aware of comment analysis. Its better example is if we want to buy gadgets online or we want to watch a movie, we first see comments or ratings of that product or movie. For this, every website asks every customer to give their feedback which is helpful for product success or movie success. From this feedback or opinion the site owner grabs the mood of the customer. That's...
We propose a novel computationally efficient hierarchical dictionary learning (HDL) approach for data-driven unmixing and functional connectivity analysis of functional magnetic resonance imaging (fMRI) data. It is shown that by simultaneously exploiting the sparsity of the spatial brain maps and the incoherence among their evolution in time or task functions, one can achieve better performance while...
The search way of traditional search engines is the full-text retrieval based on inverted index, which is also the retrieval way used to query the index database based on the search statement, and this doesn't make good use of the meaning expressed by the search statements, thereby failing to accurately identify the specific demand of users, and this is bound to give users greater search costs. The...
Classical dictionary learning algorithms that rely on a single source of information have been successfully used for the discriminative tasks. However, exploiting multiple sources has demonstrated its effectiveness in solving challenging real-world situations. We propose a new framework for feature fusion to achieve better classification performance as compared to the case where individual sources...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.