Information filtering using SVD and ICA

We propose an information filtering system for documents by a user profile using latent semantics obtained by singular value decomposition (SVD) and independent component analysis (ICA). In information filtering systems, it is useful to analyze the latent semantics of documents. ICA is one method to analyze the latent semantics. We assume that topics are independent of each other. Hence, when ICA is applied to documents, we obtain the topics included in the documents. By using SVD remove noises before applying ICA, we can improve the accuracy of topic extraction. By representation of the documents with those topics, we improve recommendations by the user profile. In addition, we construct a user profile with a genetic algorithm (GA) and evaluate it by 11-point average precision. We carried out an experiment using a test collection to confirm the advantages of the proposed method. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

偏振与ICA相结合的深度信息获取方法

针对水下图像中存在的图像退化现象,提出了一种将偏振与ICA技术相结合的深度信息获取方法。该方法先依据ICA模型对获取的偏振图像进行处理,构造已知偏振信息与水体后向散射光偏振度之间的线性关系,通过对获取的目标函数进行优化求解出水体后向散射光偏振度,最后采用软抠图法进行优化得到场景深度信息。实验结果表明,此方法在水下场景中获取的深度图像包含更多的目标信息,且运行效率高。     

采用图模型的个性化标签推荐方法

现有的标签推荐方法大多根据标签在对象中出现的次数来表示用户,标签与资源之间的关系。这种方法对标签信息的利用过于简单,导致最终的推荐结果的准确度和召回率不高。基于这个问题,提出一种采用图模型的个性化标签推荐方法,将用户、标签和资源三者的关系转换成一个三元无向图。对图中相邻顶点的处理采用一种综合的权重衡量方法,而不相邻顶点的关系采用最短路径思想得出。既考虑标签与用户的关系,又考虑标签与资源的关系给出最后的标签推荐方法。将该方法与现存的标签推荐方法做比较。实验采用的数据来自CiteULike。实验结果表明,该方法能够显著地提高推荐结果的召回率,准确性等。     

Travel route recommendation using geotagged photos

We propose a travel route recommendation method that makes use of the photographers’ histories as held by social photo-sharing sites. Assuming that the collection of each photographer’s geotagged photos is a sequence of visited locations, photo-sharing sites are important sources for gathering the location histories of tourists. By following their location sequences, we can find representative and diverse travel routes that link key landmarks. Recommendations are performed by our photographer behavior model, which estimates the probability of a photographer visiting a landmark. We incorporate user preference and present location information into the probabilistic behavior model by combining topic models and Markov models. Based on the photographer behavior model, proposed route recommendation method outputs a set of personalized travel plans that match the user’s preference, present location, spare time and transportation means. We demonstrate the effectiveness of the proposed method using an actual large-scale geotag dataset held by Flickr in terms of the prediction accuracy of travel behavior.     

A real-time recommendation engine using lambda architecture

In a data science theory, the recommended methodology is one of the most popular theories and has been deployed in many real industries. However, one of the most challenging problems these days is how to recommend items with massively streaming data. Therefore, this paper aims to do a real-time recommendation engine using the Lambda architecture. The Apache Hadoop and Apache Spark frameworks were used in this research to process the MovieLens dataset comprised 100 K and 20 M ratings from the GroupLens research. Using alternating least squares (ALS) and k-means algorithms, the top K recommendation movies and the top K trending movies for each user were shown as results. Additionally, the mean squared error (MSE) and within cluster sum of squared error (WCSS) had been computed to evaluate the performance of the ALS and k-means algorithms, sequentially. The results showed that they are acceptable since the MSE and WCSS values are low when comparing to the size of data. However, they can still be improved by tuning some parameters.     

Socialized healthcare service recommendation using deep learning

Socialized recommender system recommends reliable healthcare services for users. Ratings are predicted on the healthcare services by merging recommendations given by users who has social relations with the active users. However, existing works did not consider the influence of distrust between users. They recommend items only based on the trust relations between users. We therefore propose a novel deep learning-based socialized healthcare service recommender model, which recommends healthcare services with recommendations given by recommenders with both trust relations and distrust relations with the active users. The influences of recommenders, considering both the node information and the structure information, are merged via the deep learning model. Experimental results show that the proposed model outperforms the existing works on prediction accuracy and prediction coverage simultaneously, even for cold start users or users with very sparse trust relations. It is also computational less expensive.

     

基于ICA的滑动平均序列叠加过程的分解与复原

研究由若干个滑动平均(MA)信号序列叠加形成的多道时间序列的分解与复原问题。 首先从信号的独立性出发,利用信号的高阶统计信息,采用独立成分分析(ICA)中的固定点(Fixed Point)算法将混合信号进行分离,然后设计了一种基于高阶统计量的MA模型的自适应辨识算法,算法在每次迭代中先估计MA的阶数,再估计MA的参数,由选用的线性方程组保证了参数的唯一可辨识性。最后通过模拟实验验证了该方法的有效性。     

信息检索分类系统中过滤推荐算法研究

针对信息检索分类技术发展的需求,本文通过对协同过滤推荐算法的综述,提出传统过滤算法无法适用于用户多兴趣下的推荐问题进行了剖析,提出了一种基于用户聚类的协同过滤推荐改进算法,提高聚类的精确性,根据用户兴趣相似的特征改进计算用户相似性的方法。     

Pixel-based and region-based image fusion schemes using ICA bases

The task of enhancing the perception of a scene by combining information captured by different sensors is usually known as image fusion. The pyramid decomposition and the Dual-Tree Wavelet Transform have been thoroughly applied in image fusion as analysis and synthesis tools. Using a number of pixel-based and region-based fusion rules, one can combine the important features of the input images in the transform domain to compose an enhanced image. In this paper, the authors test the efficiency of a transform constructed using Independent Component Analysis (ICA) and Topographic Independent Component Analysis bases in image fusion. The bases are obtained by offline training with images of similar context to the observed scene. The images are fused in the transform domain using novel pixel-based or region-based rules. The proposed schemes feature improved performance compared to traditional wavelet approaches with slightly increased computational complexity.     

结合纯像元提取和ICA的高光谱降维方法*

摘要：为了实现高光谱降维并保留重要的光谱特征,通过独立分量分析（Independent Component Analysis, ICA）混合模型和高光谱线性模型的对比分析,提出了结合纯像元提取和ICA的高光谱数据降维方法。该方法通过估计虚拟维数(Virtual Dimensionality, VD)确定特征个数,采用自动目标生成过程（Automatic Target Generation Process, ATGP）从原始数据中提取纯像元向量,作为ICA算法的初始化向量,以负熵为目标函数产生独立分量,并通过高阶统计量筛选实现高光谱数据的降维。分类实验结果表明,该方法不仅解决了传统ICA的随机排序问题,而且与经典降维算法主分量分析（Principal Components Analysis, PCA）相比,分类精度提高了6.83%,在大大降低高光谱数据量的情况下很好的保留了高光谱数据的特征,有利于数据的后续分析和应用。     

Content-based filtering for recommendation systems using multiattribute networks

Content-based filtering (CBF), one of the most successful recommendation techniques, is based on correlations between contents. CBF uses item information, represented as attributes, to calculate the similarities between items. In this study, we propose a novel CBF method that uses a multiattribute network to effectively reflect several attributes when calculating correlations to recommend items to users. In the network analysis, we measure the similarities between directly and indirectly linked items. Moreover, our proposed method employs centrality and clustering techniques to consider the mutual relationships among items, as well as determine the structural patterns of these interactions. This mechanism ensures that a variety of items are recommended to the user, which improves the performance. We compared the proposed approach with existing approaches using MovieLens data, and found that our approach outperformed existing methods in terms of accuracy and robustness. Our proposed method can address the sparsity problem and over-specialization problem that frequently affect recommender systems. Furthermore, the proposed method depends only on ratings data obtained from a user's own past information, and so it is not affected by the cold start problem.     

基于图聚类的协同标记系统资源个性推荐

协同标记系统允许用户自由标记系统资源,但也由此产生了同义标签和多义标签问题,这使得如何利用用户标签构成的用户概貌信息进行个性化资源推荐成为一个难题。为此首先基于图聚类算法把系统中语义相近的标签构成聚类,然后以标签聚类为中介衡量特定用户和资源的相关度。在BibSonomy和Delicious两个数据集上进行了测试,并和另外两种算法进行了对比。实验结果显示应用该算法产生的推荐,其性能优于对比算法,在主题宽泛的系统中效果尤为明显。说明协同标记系统首先进行标签聚类是产生个性化资源推荐的重要方法。     

面向特征的ICA和HP滤波实现视频异常事件检测

针对室外环境下光照亮度变化、阴影和树木遮挡等问题,对利用隐马尔可夫模型进行视频异常事件检测的影响,提出基于独立分量分析（ICA）和HP（Hodrick-Prescott）滤波器的隐马尔可夫模型视频异常事件检测方法。该方法首先利用ICA构造正常视频的特征子空间,将图像序列投影到特征子空间上得到投影序列,实现数据降维;然后利用HP滤波器滤除投影序列中环境变化引起的趋势分量;最终克服不利的环境因素,有效改善隐马尔可夫模型的视频异常事件检测性能。机动车辆禁行路段视频的检测实验表明,该方法能够在复杂的室外环境下较好地检测出异常事件。     

Bounded multivariate generalized Gaussian mixture model using ICA and IVA

Pattern Analysis and Applications - A bounded multivariate generalized Gaussian mixture model with a full covariance matrix is proposed for modeling data in a bounded support region. For model...     

Database retrieval for similar images using ICA and PCA bases

Similar-image retrieval systems are newly presented and examined. The systems use ICA bases (independent component analysis bases) or PCA bases (principal component analysis bases). These bases can contain source image's information, however, the indeterminacy of ordering and amplitude on the bases exists due to the PCA and ICA problem formulation per se. But, this paper successfully avoids this difficulty by using weighted inner products of similar bases. A set of opinion test is carried out on 18 systems according to the combination of {similarity measures (ICA, PCA, color histogram), color spaces (RGB, YIQ, HSV), filtering (with, without)}. The color histogram method is a traditional method. The opinion test shows that the presented method of {ICA, HSV, without filtering} is the best. Runners-up are {ICA, HSV or RGB or YIQ, with filtering}. The traditional method is judged to be much inferior. Thus, this paper's method is found quite effective to the similar-image retrieval from large databases.     

Item recommendation using tag emotion in social cataloging services

Due to the overload of contents, the user suffers from difficulty in selecting items. The social cataloging services allow users to consume items and share their opinions, which influences in not only oneself but other users to choose new items. The recommendation system reduces the problem of the choice by recommending the items considering the behavior of the people and the characteristics of the items.In this study, we propose a tag-based recommendation method considering the emotions reflected in the user’s tags. Since the user’s estimation of the item is made after consuming the item, the feelings of the user obtained during consuming are directly reflected in ratings and tags. The rating has overall valence on the item, and the tag represents the detailed feelings. Therefore, we assume that the user’s rating for an item is the basic emotion of the tag attached to the item, and the emotion of tag is adjusted by the unique emotion value of the tag. We represent the relationships between users, items, and tags as a three-order tensor and apply tensor factorization. The experimental results show that the proposed method achieves better recommendation performance than baselines.     

Context-aware recommendation using rough set model and collaborative filtering

Context has been identified as an important factor in recommender systems. Lots of researches have been done for context-aware recommendation. However, in current approaches, the weights of contextual information are the same, which limits the accuracy of the results. This paper aims to propose a context-aware recommender system by extracting, measuring and incorporating significant contextual information in recommendation. The approach is based on rough set theory and collaborative filtering. It involves a three-steps process. At first, significant attributes to represent contextual information are extracted and measured to identify recommended items based on rough set theory. Then the users’ similarity is measured in a target context consideration. Furthermore collaborative filtering is adopted to recommend appropriate items. The evaluation experiments show that the proposed approach is helpful to improve the recommendation quality.     

A novel stock recommendation system using Guba sentiment analysis

Investment recommendation has been one of the hottest topics in the finance area which can help investors to get more profits and to avoid loss. Existing recommendation systems mostly depend on analysis of trading data and company profit prediction. Though many works show that there is a positive correlation between investors’ sentiment and the finance market trends, few recommendation theories have been built based on sentiment. The primary reason is the difficulty to measure investors’ sentiment. In this work, a novel stock recommendation system is developed based on a proposed theory concerning the correlation between Guba-based sentiment of the retail investors and the stock market trends in China. To verify four hypotheses of the theory, a novel method is proposed to measure the investors’ sentiment by exploiting the large volumes of emotion enriched texts posted in Guba, which is online social platform for individual investors to share news and opinions concerning their favorite stocks. Results show the correctness of the proposed theory: (1) there is a positive correlation between Guba-based sentiment and the stock market trends; 2) the higher the post volumes and agreement, more proficiency the bullishness would be; and (3) a long-lasting negative Guba-based sentiment indicates the arrival of the bear market. The proposed recommendation system consists of three criteria accordingly to ensure the portfolio to meet requirements of the theory. Finally, experiments are implemented using the real data of Chinese stock market from March 2009 to March 2016 and the results show the effectiveness of the proposed system in recommending lucrative stocks and the theoretical cumulate profit is about eight times of the CSI300 in the period.     

使用贝叶斯网络的社交网络信任驱动推荐方法

为处理推荐行为来源复杂、路径多样、不信任陌生推荐等问题,提出一种在社交网络中信任驱动推荐方法。该方法利用贝叶斯网络,计算用户评分的先验概率分布以及朋友之间的联合条件概率,预测用户在该环境下的评分并将推荐给用户。在信任驱动推荐过程中,预测评分既考虑到用户的偏好,也考虑到用户的社会关系;此外,用户的信息交换只限于朋友之间,能够有效保护用户的隐私。实验结果表明,所提出的推荐方法在预测准确率和推荐覆盖率上具有良好的性能。