If we already know a particular concept representations by considering the semantic relations between such as pets, we can describe a new concept such as dogs words. Specifically, given as input a relational graph, by stating the semantic relations that the new concept shares a directed labelled weighted graph where vertices represent with the existing concepts such as dogs belongs-to pets. Alternatively, words and edges represent numerous semantic relations we could describe a novel concept by listing all that exist between the corresponding words, we consider the the attributes it shares with existing concepts. In our example, problem of learning a vector representation for each vertex we can describe the concept dog by listing attributes (word) in the graph and a matrix representation for each label such as mammal, carnivorous, and domestic animal that it type (pattern). The learnt word representations are evaluated shares with another concept such as the cat. Therefore, both for their accuracy by using them to solve semantic word attributes and relations can be considered as alternative descriptors analogy questions on a benchmark dataset. of the same knowledge. This close connection between Our task of learning word attributes using relations between attributes and relations can be seen in knowledge representation words is challenging because of several reasons. First, schemes such as predicate logic, where attributes there can be multiple semantic relations between two words.

Large-scale distributed computing has made available the resources necessary to solve "AI-hard" problems. As a result, it becomes feasible to automate the processing of such problems, but accuracy is not very high due to the conceptual difficulty of these problems. In this paper, we integrated crowdsourcing with MapReduce to provide a scalable innovative human-machine solution to AI-hard problems, which is called CrowdMR. In CrowdMR, the majority of problem instances are automatically processed by machine while the troublesome instances are redirected to human via crowdsourcing. The results returned from crowdsourcing are validated in the form of CAPTCHA (Completely Automated Public Turing test to Tell Computers and Humans Apart) before adding to the output. An incremental scheduling method was brought forward to combine the results from machine and human in a "pay-as-you-go" way.

Multi-label image classification is of significant interest due to its major role in real-world web image analysis applications such as large-scale image retrieval and browsing. Recently, matrix completion (MC) has been developed to deal with multi-label classification tasks. MC has distinct advantages, such as robustness to missing entries in the feature and label spaces and a natural ability to handle multi-label problems. However, current MC-based multi-label image classification methods only consider data represented by a single-view feature, therefore, do not precisely characterize images that contain several semantic concepts. An intuitive way to utilize multiple features taken from different views is to concatenate the different features into a long vector; however, this concatenation is prone to over-fitting and leads to high time complexity in MC-based image classification. Therefore, we present a novel multi-view learning model for MC-based image classification, called low-rank multi-view matrix completion (lrMMC), which first seeks a low-dimensional common representation of all views by utilizing the proposed low-rank multi-view learning (lrMVL) algorithm. In lrMVL, the common subspace is constrained to be low rank so that it is suitable for MC. In addition, combination weights are learned to explore complementarity between different views. An efficient solver based on fixed-point continuation (FPC) is developed for optimization, and the learned low-rank representation is then incorporated into MC-based image classification. Extensive experimentation on the challenging PASCAL VOC' 07 dataset demonstrates the superiority of lrMMC compared to other multi-label image classification approaches.

Due to the simplicity and efficiency, many hashing methods have recently been developed for large-scale similarity search. Most of the existing hashing methods focus on mapping low-level features to binary codes, but neglect attributes that are commonly associated with data samples. Attribute data, such as image tag, product brand, and user profile, can represent human recognition better than low-level features. However, attributes have specific characteristics, including high-dimensional, sparse and categorical properties, which is hardly leveraged into the existing hashing learning frameworks. In this paper, we propose a hashing learning framework, Probabilistic Attributed Hashing (PAH), to integrate attributes with low-level features. The connections between attributes and low-level features are built through sharing a common set of latent binary variables, i.e. hash codes, through which attributes and features can complement each other. Finally, we develop an efficient iterative learning algorithm, which is generally feasible for large-scale applications. Extensive experiments and comparison study are conducted on two public datasets, i.e., DBLP and NUS-WIDE. The results clearly demonstrate that the proposed PAH method substantially outperforms the peer methods.

In this paper, we propose and address a novel task of recommending quotes for writing. Quote is short for quotation, which is the repetition of someone else’s statement or thoughts. It is a common case in our writing when we would like to cite someone’s statement, like a proverb or a statement by some famous people, to make our composition more elegant or convincing. However, sometimes we are so eager to make a citation of quote somewhere, but have no idea about the relevant quote to express our idea. Because knowing or remembering so many quotes is not easy, it is exciting to have a system to recommend relevant quotes for us while writing. In this paper we tackle this appealing AI task, and build up a learning framework for quote recommendation. We collect abundant quotes from the Internet, and mine real contexts containing these quotes from large amount of electronic books, to build up a dataset for experiments. We explore the particular features of this task, and propose a few useful features to model the characteristics of quotes and the relevance of quotes to contexts. We apply a supervised learning to rank model to integrate multiple features. Experiment results show that, our proposed approach is appropriate for this task and it outperforms other recommendation methods.

Social media platforms are often used by people to express their needs and desires. Such data offer great opportunities to identify users’ consumption intention from user-generated contents, so that better tailored products or services can be recommended. However, there have been few efforts on mining commercial intents from social media contents. In this paper, we investigate the use of social media data to identify consumption intentions for individuals. We develop a Consumption Intention Mining Model (CIMM) based on convolutional neural network (CNN), for identifying whether the user has a consumption intention. The task is domain-dependent, and learning CNN requires a large number of annotated instances, which can be available only in some domains. Hence, we investigate the possibility of transferring the CNN mid-level sentence representation learned from one domain to another by adding an adaptation layer. To demonstrate the effectiveness of CIMM, we conduct experiments on two domains. Our results show that CIMM offers a powerful paradigm for effectively identifying users’ consumption intention based on their social media data. Moreover, our results also confirm that the CNN learned in one domain can be effectively transferred to another domain. This suggests that a great potential for our model to significantly increase effectiveness of product recommendations and targeted advertising.

With the recent growth of online content on the Web, there have been more user generated data with noisy and missing labels, e.g., social tags and voted labels from Amazon's Mechanical Turks. Most of machine learning methods, which require accurate label sets, could not be trusted when the label sets were yet unreliable. In this paper, we provide a text label refinement algorithm to adjust the labels for such noisy and missing labeled datasets. We assume that the labeled sets can be refined based on the labels with certain confidence, and the similarity between data being consistent with the labels. We propose a label smoothness ratio criterion to measure the smoothness of the labels and the consistency between labels and data. We demonstrate the effectiveness of the label refining algorithm on eight labeled document datasets, and validate that the results are useful for generating better labels.

Gaussian process (GP) regression is a widely used method for non-linear prediction.The performance of the GP regression depends on whether it can properly capture the covariance structure of target variables, which is represented by kernels between input data.However, when the input is represented as a set of features, e.g. bag-of-words, it is difficult to calculate desirable kernel values because the co-occurrence of different but relevant words cannot be reflected in the kernel calculation.To overcome this problem, we propose a Gaussian process latent variable set model (GP-LVSM), which is a non-linear regression model effective for bag-of-words data.With the GP-LVSM, a latent vector is associated with each word, and each document is represented as a distribution of the latent vectors for words appearing in the document. We efficiently represent the distributions by using the framework of kernel embeddings of distributions that can hold high-order moment information of distributions without need for explicit density estimation.By learning latent vectors so as to maximize the posterior probability, kernels that reflect relations between words are obtained, and also words are visualized in a low-dimensional space.In experiments using 25 item review datasets, we demonstrate the effectiveness of the GP-LVSM in prediction and visualization.

In multi-task learning (MTL), multiple related tasks are learned jointly by sharing information across them. Many MTL algorithms have been proposed to learn the underlying task groups. However, those methods are limited to learn the task groups at only a single level, which may be not sufficient to model the complex structure among tasks in many real-world applications. In this paper, we propose a Multi-Level Task Grouping (MeTaG) method to learn the multi-level grouping structure instead of only one level among tasks. Specifically, by assuming the number of levels to be H, we decompose the parameter matrix into a sum of H component matrices, each of which is regularized with a l2 norm on the pairwise difference among parameters of all the tasks to construct level-specific task groups. For optimization, we employ the smoothing proximal gradient method to efficiently solve the objective function of the MeTaG model. Moreover, we provide theoretical analysis to show that under certain conditions the MeTaG model can recover the true parameter matrix and the true task groups in each level with high probability. We experiment our approach on both synthetic and real-world datasets, showing competitive performance over state-of-the-art MTL methods.

Learning from imbalanced data sets is one of the challenging problems in machine learning, which means the number of negative examples is far more than that of positive examples. The main problems of existing methods are: (1) The degree of re-sampling, a key factor greatly affecting performance, needs to be pre-fixed, which is difficult to make the optimal choice; (2) Many useful negative samples are discarded in under-sampling; (3) The effectiveness of algorithm-level methods are limited because they just use the original training data for single classifier. To address the above issues, a novel approach of adaptive sampling with optimal cost is proposed for class-imbalance learning in this paper. The novelty of the proposed approach mainly lies in: adaptively over-sampling the minority positive examples and under-sampling the majority negative examples, forming different sub-classifiers by different subsets of training data with the best cost ratio adaptively chosen, and combining these sub-classifiers according to their accuracy to create a strong classifier. It aims to make full use of the whole training data and improve the performance of class-imbalance learning classifier. The solid experiments are conducted to compare the performance between the proposed approach and 12 state-of-the-art methods on challenging 16 UCI data sets on 3 evaluation metrics, and the results show the proposed approach can achieve superior performance in class-imbalance learning.