Similarity search, or finding approximate nearest neighbors, is an important technique for many applications. Many recent research demonstrate that hashing methods can achieve promising results for large scale similarity search due to its computational and memory efficiency.

Multiple Instance Learning (MIL) methods generally represent each example as a collection of instances such that the features for local objects can be better captured, whereas traditional learning methods typically extract a global feature vector for each example as an integral part. However, there is limited research work on which of the two learning scenarios performs better. This paper proposes a novel framework – \emphMultiple Instance LEArning with Global Embedding (MILEAGE), in which the global feature vectors for traditional learning methods are integrated into the MIL setting. MILEAGE can leverage the benefits derived from both learning settings. Within the proposed framework, a large margin method is formulated. In particular, the proposed method adaptively tunes the weights on the two different kinds of feature representations (i.e., global and multiple instance) for each example and trains the classifier simultaneously. An alternative algorithm is proposed to solve the resulting optimization problem, which extends the bundle method to the non-convex case. Some important properties of the proposed method, such as the convergence rate and the generalization error rate, are analyzed. A series of experiments have been conducted to demonstrate the advantages of the proposed method over several state-of-the-art multiple instance and traditional learning methods.

In Multiple Instance Learning (MIL), each entity is normally expressed as a set of instances. Most of the current MIL methods only deal with the case when each instance is represented by one type of features. However, in many real world applications, entities are often described from several different information sources/views. For example, when applying MIL to image categorization, the characteristics of each image can be derived from both its RGB features and SIFT features. Previous research work has shown that, in traditional learning methods, leveraging the consistencies between different information sources could improve the classification performance drastically.

It is an important research problem to design efficient and effective solutions for large scale similarity search. One popular strategy is to represent data examples as compact binary codes through semantic hashing, which has produced promising results with fast search speed and low storage cost. Many existing semantic hashing methods generate binary codes for documents by modeling document relationships based on similarity in a keyword feature space. Two major limitations in existing methods are: (1) Tag information is often associated with documents in many real world applications, but has not been fully exploited yet; (2) The similarity in keyword feature space does not fully reflect semantic relationships that go beyond keyword matching.

The increasing volume of information generated on micro-blogging sites such as Twitter raises several challenges to traditional text mining techniques. First, most texts from those sites are abbreviated due to the constraints of limited characters in one post; second, the input usually comes in streams of large-volumes. Therefore, it is of significant importance to develop effective and efficient representations of abbreviated texts for better filtering and mining. In this paper, we introduce a novel transfer learning approach, namely transfer latent semantic learning, that utilizes a large number of related tagged documents with rich information from other sources (source domain) to help build a robust latent semantic space for the abbreviated texts (target domain). This is achieved by simultaneously minimizing the document reconstruction error and the classification error of the labeled examples from the source domain by building a classifier with hinge loss in the latent semantic space. We demonstrate the effectiveness of our method by applying them to the task of classifying and tagging abbreviated texts. Experimental results on both synthetic datasets and real application datasets, including Reuters-21578 and Twitter data, suggest substantial improvements using our approach over existing ones.

Transductive transfer learning is one special type of transfer learning problem, in which abundant labeled examples are available in the source domain and only unlabeled examples are available in the target domain. It easily finds applications in spam filtering, microblogging mining and so on. In this paper, we propose a general framework to solve the problem by mapping the input features in both the source domain and target domain into a shared latent space and simultaneously minimizing the feature reconstruction loss and prediction loss. We develop one specific example of the framework, namely latent large-margin transductive transfer learning (LATTL) algorithm, and analyze its theoretic bound of classification loss via Rademacher complexity. We also provide a unified view of several popular transfer learning algorithms under our framework. Experiment results on one synthetic dataset and three application datasets demonstrate the advantages of the proposed algorithm over the other state-of-the-art ones.

Transfer learning has been proposed to address the problem of scarcity of labeled data in the target domain by leveraging the data from the source domain. In many real world applications, data is often represented from different perspectives, which correspond to multiple views. For example, a web page can be described by its contents and its associated links. However, most existing transfer learning methods fail to capture the multi-view {nature}, and might not be best suited for such applications.

Most traditional supervised learning methods are developed to learn a model from labeled examples and use this model to classify the unlabeled ones into the same label space predefined by the models. However, in many real world applications, the label spaces for both the labeled/training and unlabeled/testing examples can be different. To solve this problem, this paper proposes a novel notion of Serendipitous Learning (SL), which is defined to address the learning scenarios in which the label space can be enlarged during the testing phase. In particular, a large margin approach is proposed to solve SL. The basic idea is to leverage the knowledge in the labeled examples to help identify novel/unknown classes, and the large margin formulation is proposed to incorporate both the classification loss on the examples within the known categories, as well as the clustering loss on the examples in unknown categories. An efficient optimization algorithm based on CCCP and the bundle method is proposed to solve the optimization problem of the large margin formulation of SL. Moreover, an efficient online learning method is proposed to address the issue of large scale data in online learning scenario, which has been shown to have a guaranteed learning regret. An extensive set of experimental results on two synthetic datasets and two datasets from real world applications demonstrate the advantages of the proposed method over several other baseline algorithms. One limitation of the proposed method is that the number of unknown classes is given in advance. It may be possible to remove this constraint if we model it by using a non-parametric way. We also plan to do experiments on more real world applications in the future.

Most existing Multiple-Instance Learning (MIL) algorithms assume data instances and/or data bags are independently and identically distributed. But there often exists rich additional dependency/structure information between instances/bags within many applications of MIL. Ignoring this structure information limits the performance of existing MIL algorithms. This paper explores the research problem as multiple instance learning on structured data (MILSD) and formulates a novel framework that considers additional structure information. In particular, an effective and efficient optimization algorithm has been proposed to solve the original non-convex optimization problem by using a combination of Concave-Convex Constraint Programming (CCCP) method and an adapted Cutting Plane method, which deals with two sets of constraints caused by learning on instances within individual bags and learning on structured data. Our method has the nice convergence property, with specified precision on each set of constraints. Experimental results on three different applications, i.e., webpage classification, market targeting, and protein fold identification, clearly demonstrate the advantages of the proposed method over state-of-the-art methods.

Similarity search applications with a large amount of text and image data demands an efficient and effective solution. One useful strategy is to represent the examples in databases as compact binary codes through semantic hashing, which has attracted much attention due to its fast query/search speed and drastically reduced storage requirement. All of the current semantic hashing methods only deal with the case when each example is represented by one type of features. However, examples are often described from several different information sources in many real world applications. For example, the characteristics of a webpage can be derived from both its content part and its associated links.

Document clustering is a popular research topic, which aims to partition documents into groups of similar objects (i.e., clusters), and has been widely used in many applications such as automatic topic extraction, document organization and filtering. As a recently proposed concept, Universum is a collection of "non-examples" that do not belong to any concept/cluster of interest. This paper proposes a novel document clustering technique -- Document Clustering with Universum, which utilizes the Universum examples to improve the clustering performance. The intuition is that the Universum examples can serve as supervised information and help improve the performance of clustering, since they are known not belonging to any meaningful concepts/clusters in the target domain. In particular, a maximum margin clustering method is proposed to model both target examples and Universum examples for clustering. An extensive set of experiments is conducted to demonstrate the effectiveness and efficiency of the proposed algorithm.

In multiple instance learning problems, patterns are often given as bags and each bag consists of some instances. Most of existing research in the area focuses on multiple instance classification and multiple instance regression, while very limited work has been conducted for multiple instance clustering (MIC). This paper formulates a novel framework, maximum margin multiple instance clustering (M(3)IC), for MIC. However, it is impractical to directly solve the optimization problem of M(3)IC. Therefore, M(3)IC is relaxed in this paper to enable an efficient optimization solution with a combination of the constrained concave-convex procedure and the cutting plane method. Furthermore, this paper presents some important properties of the proposed method and discusses the relationship between the proposed method and some other related ones. An extensive set of empirical results are shown to demonstrate the advantages of the proposed method against existing research for both effectiveness and efficiency.

Modeling the response time of search engines is an important task for many applications such as resource selection in federated text search. Limited research has been conducted to address this task. Prior research calculated the search response time of all queries in the same way either with the average response time of several sample queries or with a single probability distribution, which is irrelevant to the characteristics of queries. However, the search response time may vary a lot for different types of queries. This paper proposes a novel query-specific and source-specific approach to model search response time. Some training data is acquired by measuring the search response time of some sample queries from a search engine. Then, a query-specific model is estimated with the training data and their corresponding response times by utilizing Ridge Regression. The obtained model can be used to predict search response times for new queries. A set of empirical studies are conducted to show the effectiveness of the proposed method.

In this paper, we propose two general multiple instance active learning (MIAL) algorithms, multiple-instance active learning with a simple margin strategy (S-MIAL) and multiple- instance active learning with fisher information (F-MIAL), and apply them to the relevance feedback in localized content based image retrieval (LCBIR). S-MIAL considers the most ambiguous picture as the most valuable one, while F-MIAL can utilize the fisher information and analyze the value of the unlabeled pictures by assigning different labels to them. We show that F-MIAL can be integrated more naturally into the multiple instance learning scenario. In experiments, we will show their superior performances on some real-world image datasets.

The Universum data, defined as a collection of “non-examples” that do not belong to any class of interest, have been shown to encode some prior knowledge by representing meaningful concepts in the same domain as the problem at hand. In this paper, we address a novel semi-supervised classification problem, called semi-supervised Universum, that can simultaneously utilize the labeled data, unlabeled data and the Universum data to improve the classification performance. We propose a graph based method to make use of the Universum data to help depict the prior information for possible classifiers. Like conventional graph based semi-supervised methods, the graph regularization is also utilized to favor the consistency between the labels. Furthermore, since the proposed method is a graph based one, it can be easily extended to the multiclass case. The empirical experiments on the USPS and MNIST datasets are presented to show that the proposed method can obtain superior performances over conventional supervised and semi-supervised methods.

This paper proposes a method for the multi-pitch estimation of polyphonic music signals. Instead of on the frame level, the estimation is based on the partial event, which is defined like the note event in MIDI. All partial events in a piece of music are extracted dynamically in the process of the frame by frame short time Fourier transform (STFT). For each event, net support degree received from other events is calculated and the events with the highest support degrees are selected to be the fundamental frequency (FO) events. From another point of view, the support is transferred from higher frequency partial events to lower ones and finally concentrated on the FO events. This method can estimate the number of concurrent sounds, the onset and offset times of the notes. Experiments on both randomly mixed chord signals and synthesized ensemble music signals in "wav" format are conducted and the results are promising.

In this paper we develop a new method for blind separation of temporally correlated sources, possibly dependent signals from linear mixtures of them. The proposed algorithm is based on the mutual independency of the innovations of source signals instead of original signals. This algorithm takes into account both the temporal structure and the high-order statistics of source signals and in contrast to the most known blind separation algorithms only exploiting the second order statistics or the non-Gaussianity. In this framework, a fixed-point algorithm is introduced. The fixed-point algorithm is computationally very simple, converge fast, and does not need choose any learning step sizes. Extensive computer simulations with speech signals and images confirm the validity and high performance of the proposed algorithm.