Deep Convolutional Neural Networks (CNN) have shown great success in supervised classification tasks such as character classification or dating. Deep learning methods typically need a lot of annotated training data, which is not available in many scenarios. In these cases, traditional methods are often better than or equivalent to deep learning methods. In this paper, we propose a simple, yet effective, way to learn CNN activation features in an unsupervised manner. Therefore, we train a deep residual network using surrogate classes. The surrogate classes are created by clustering the training dataset, where each cluster index represents one surrogate class. The activations from the penultimate CNN layer serve as features for subsequent classification tasks. We evaluate the feature representations on two publicly available datasets. The focus lies on the ICDAR17 competition dataset on historical document writer identification (Historical-WI). We show that the activation features we trained without supervision are superior to descriptors of state-of-the-art writer identification methods. Additionally, we achieve comparable results in the case of handwriting classification using the ICFHR16 competition dataset on historical Latin script types (CLaMM16).
Clustering has also been used to create unsupervised attributes for historical document dating in the work of al He @cite_21 . However, they use handcrafted features in conjunction with SVMs. Instead, we learn the features in an unsupervised manner using a deep CNN.
@cite_21
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Clustering is a common unsupervised learning technique used to discover group structure in a set of data. While there exist many algorithms for clustering, the important issue of feature selection, that is, what attributes of the data should be used by the clustering algorithms, is rarely touched upon. Feature selection for clustering is difficult because, unlike in supervised learning, there are no class labels for the data and, thus, no obvious criteria to guide the search. Another important problem in clustering is the determination of the number of clusters, which clearly impacts and is influenced by the feature selection issue. In this paper, we propose the concept of feature saliency and introduce an expectation-maximization (EM) algorithm to estimate it, in the context of mixture-based clustering. Due to the introduction of a minimum message length model selection criterion, the saliency of irrelevant features is driven toward zero, which corresponds to performing feature selection. The criterion and algorithm are then extended to simultaneously estimate the feature saliencies and the number of clusters. Automated unsupervised learning of topic-based clusters is used in various text data mining applications, e.g., document organization in content management, information retrieval and filtering in news aggregation services. Typically batch models are used for this purpose, which perform clustering on the document collection in aggregate. In this paper, we first analyze three batch topic models that have been recently proposed in the machine learning and data mining community – Latent Dirichlet Allocation (LDA), Dirichlet Compound Multinomial (DCM) mixtures and von-Mises Fisher (vMF) mixture models. Our discussion uses a common framework based on the particular assumptions made regarding the conditional distributions corresponding to each component and the topic priors. Our experiments on large real-world document collections demonstrate that though LDA is a good model for finding word-level topics, vMF finds better document-level topic clusters more efficiently, which is often important in text mining applications. In cases where offline clustering on complete document collections is infeasible due to resource constraints, online unsupervised clustering methods that process incoming data incrementally are necessary. To this end, we propose online variants of vMF, EDCM and LDA. Experiments on real-world streaming text illustrate the speed and performance benefits of online vMF. Finally, we propose a practical heuristic for hybrid topic modeling, which learns online topic models on streaming text data and intermittently runs batch topic models on aggregated documents offline. Such a hybrid model is useful for applications (e.g., dynamic topic-based aggregation of consumer-generated content in social networking sites) that need a good tradeoff between the performance of batch offline algorithms and efficiency of incremental online algorithms. We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models. We observe that the known problem of over-regularisation that has been shown to arise in regular VAEs also manifests itself in our model and leads to cluster degeneracy. We show that a heuristic called minimum information constraint that has been shown to mitigate this effect in VAEs can also be applied to improve unsupervised clustering performance with our model. Furthermore we analyse the effect of this heuristic and provide an intuition of the various processes with the help of visualizations. Finally, we demonstrate the performance of our model on synthetic data, MNIST and SVHN, showing that the obtained clusters are distinct, interpretable and result in achieving competitive performance on unsupervised clustering to the state-of-the-art results. Incorporating semantic knowledge from an ontology into document clustering is an important but challenging problem. While numerous methods have been developed, the value of using such an ontology is still not clear. We show in this paper that an ontology can be used to greatly reduce the number of features needed to do document clustering. Our hypothesis is that polysemous and synonymous nouns are both relatively prevalent and fundamentally important for document cluster formation. We show that nouns can be efficiently identified in documents and that this alone provides improved clustering. We next show the importance of the polysemous and synonymous nouns in clustering and develop a unique approach that allows us to measure the information gain in disambiguating these nouns in an unsupervised learning setting. In so doing, we can identify a core subset of semantic features that represent a text corpus. Empirical results show that by using core semantic features for clustering, one can reduce the number of features by 90 or more and still produce clusters that capture the main themes in a text corpus.