مدل هارمونیوم بال دو گانه بدیع با کمک 2-D تغییر شکل زیر باندهای موجک برای داده کاوی سند
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|22183||2010||10 صفحه PDF||سفارش دهید||7880 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 37, Issue 6, June 2010, Pages 4403–4412
A novel dual wing harmonium model that integrates multiple features including term frequency features and 2-D wavelet transform features into a low dimensional semantic space is proposed for the applications of document classification and retrieval. Terms are extracted from the graph representation of document by employing weighted feature extraction method. 2-D wavelet transform is used to compress the graph due to its sparseness while preserving the basic document structure. After transform, low-pass subbands are stacked to represent the term associations in a document. We then develop a new dual wing harmonium model projecting these multiple features into low dimensional latent topics with different probability distributions assumption. Contrastive divergence algorithm is used for efficient learning and inference. We perform extensive experimental verification in document classification and retrieval, and comparative results suggest that the proposed method delivers better performance than other methods.
In this paper, we consider the problem of modeling document data using multiple features. The evolution of human languages leads to a growing demand of extracting more features from documents to express rich information and different meanings of term combinations. Another demand is to find low dimensional semantic expressions of documents with integrating multiple features while preserving the essential statistical relationships between terms and documents, which is useful for facilitating processing of large corpora and dealing with data mining tasks such as classification, retrieval, summarization and plagiarism detection. Vector space model (VSM) (Salton & McGill, 1983), the most popular and widely used tf–idf scheme, uses a basic vocabulary of “words” or “terms” for feature description. The term frequency (tf) is the number of occurrences of each term, and the inverse-document-frequency (idf) is a function of the number of document where a term took place. A term weighted vector is then constructed for each document using tf and idf. Similarity between two documents is then measured using ‘cosine’ distance or any other distance functions ( Zobel & Moffat, 1998). Thus, the VSM scheme reduces arbitrary length of term vector in each document to fixed length. But a lengthy vector is required for describing the frequency information of terms, because the number of words involved is usually huge. This causes a significant increase of computational burden making the VSM model impractical for large corpus. In addition, VSM scheme reveals little statistical structure about a document because of only using low level document features (i.e. term frequency). Latent semantic indexing (LSI) ( Deerwester & Dumais, 1990), an extension from VSM model, maps the documents and terms to a latent space representation by performing a linear projection to compress the feature vector of the VSM model into low dimension. Singular value decomposition (SVD) is employed to find the hidden semantic association between term and document for conceptual indexing. In addition to feature compression, LSI model is useful in encoding the semantics ( Berry, Dumais, & O’Brien, 1995). A step forward in probabilistic models is probabilistic latent semantic indexing (PLSI) ( Hofmann et al., 1999) that defines a proper generative model of data to model each word in a document as a sample from a mixture distribution and develop factor representations for mixture components. Chien and Wu (2008) further developed an adaptive Bayesian PLSI for incremental learning and corrective training that was designed to retrieve relevant documents in the presence of changing domain or topics. By realizing overfitting problems and the lack of description at the level of documents in PLSI, Blei, Ng, and Jordan (2003) introduced an extension in this regard, latent Dirichlet allocation (LDA). LDA is viewed as a three-level hierarchical Bayesian model, in which each document is modeled as a finite mixture over an underlying set of topics. Using probabilistic approach is able to provide an explicit representation of a document. Compared with LDA, exponential family harmonium (EFH) model ( Welling, Rosen-Zvi, & Hinton, 2004) is an alternative two-layer model using exponential family distributions and the semantics of undirected models for document retrieval. EFH is able to reduce the feature dimension significantly using a few latent topics (or hidden units) to represent a document. But EFH is only practical for term observations with very few states (e.g. binary). By following the general architecture of EFH, Gehler, Holub, and Welling (2006) then developed a rate adapting Poisson (RAP) model that couples latent topics to term counts using a conditional Poisson distribution for observed count data and conditional binomial distribution for latent topics involving a weight matrix, respectively. Xing et al., 2005 and Yang et al., 2008 developed dual wing harmonium (DWH) and hierarchical harmonium (HH) to model associated data from multiple sources jointly for the special applications in video classification. In their DWH model, the authors directly treated the term counts via Bernoulli distribution whose rates are determined by the combination of latent topics and the whole image color histogram via a multivariate Gaussian distribution whose mean is determined in the same way. These approaches only use independent word as feature unit, and these feature extraction schemes are a rough representation of a document. However, in real applications, it is important to consider the document structure and term associations in each document. For example, two documents containing similar term frequencies may be contextually different when the spatial distribution of terms are very different, i.e., school, computer, and science means very different when they appear in different parts of a document compared to the case of school of computer science that appear together. Thus, using only term frequency information from the “bag of words” model is not the most effective way to account contextual similarity that includes the word inter-connections and spatial distribution of words throughout the document. By realizing this problem, Chow and Rahman (2009) introduced a tree structure and used multilayer self-organizing map (SOM) for document retrieval and plagiarism detection with promising results. In this paper, we try to use graph, wavelet compression and statistical data reduction with multiple features to improve document data mining performance. First, we introduce undirected graph for document representation that results in more semantic information to be included. Terms are extracted by using weighted feature extraction method. Each document graph is then compressed by employing 2-D wavelet transform. We use stacked low-pass subbands with preserving document structure as term associations features. Motivated by ideas in reference ( Xing et al., 2005), we then develop a novel dual wing harmonium (DWH) to generate distributed latent representations of documents with modeling multiple features jointly. We model term counts (term frequency, TF) with a conditional Poisson distribution and wavelet transform (WT) features with a conditional multivariate Gaussian distribution, respectively. Latent topics are treated as a conditional binomial distribution involving weighted matrixes and multiple features. DWH in this paper is an extension of RAP ( Gehler et al., 2006) model with combining multiple features into document latent representation framework. The performance of DWH model is investigated in the applications of document classification and retrieval. We report accuracy results comparing with RAP model and traditional LSI. We also investigate the influence of the number of latent topics, different inference methods and normalization parameter for balancing weights of TF feature and WT feature. Therefore, the contribution of this paper is twofold. First, we propose a multiple feature extraction framework for representing a document combined with traditional TF feature and WT feature extracted from graph compression using 2-D wavelet transform. Multiple features are able to express more semantic information of the terms associations and spatial distribution throughout document. Second, a new DWH model is developed to project multiple features to low dimensional latent representations capturing the semantics hidden in documents. These latent topics are then applied to document classification and retrieval with promising results. The remaining sessions of this paper are organized as follows. Multiple features extraction framework is introduced in Section 2. In Section 3, a new DWH model is described in details with brief introduction to EFH and RAP models. Section 4 introduces contrastive divergence algorithm for DWH learning and inference. Application results together with discussions are presented in Section 5. The paper ends with conclusions and future work propositions in Section 6.
نتیجه گیری انگلیسی
A novel dual wing harmonium (DWH) model using multiple features is proposed for modeling the document data with applications to classification and retrieval tasks. This DWH model integrates multiple document features into low dimensional semantic space with few latent topics for document representation. First, TF features are extracted from documents by using weighted feature extraction method. We then formed an undirected graph to represent each document based on extracted terms as nodes. In order to improve the computational efficiency, term graph compression is conducted by employing 2-D wavelet transform. Thus, the term graph becomes more compact while preserving the basic graph structure. Multiple features that consist of TF features and WT features constructed by stacked low-pass subbands are then as inputs of DWH model. DWH model extends the basic RAP model to two wings by using different conditional probability distributions. It does not only include the properties of RAP, but also contains capability to capture terms associations information. Two application examples corroborate the efficiency of our method. Our future work will include enriching our database to support the assumption on terms associations and further studying the stability of inference algorithms of harmonium models.