Incremental Version Research Articles

Incremental willingness to pay: a theoretical and empirical exposition

Applications of willingness to pay (WTP) have shown the difficultly to discriminate between various options. This reflects the problem of embedding in both its specific sense, of options being nested within one another, and its more general sense, whereby respondents cannot discriminate between close substitutes or between more-disparate rivals for the same budget. Furthermore, high proportions of reversals between WTP-value and simple preference based rankings of options are often highlighted. Although an incremental WTP approach was devised to encourage more differentiated answers and a higher degree of consistency among respondents, a theoretical basis for this approach has not been elucidated, and there is little evidence to show that this approach might indeed achieve greater consistency between explicit and implicit rankings inferred from WTP values. We address both these issues. Following our theoretical exposition, standard and incremental approaches were compared with explicit ranking in a study assessing preferences for different French emergency care services. 280 persons, representative of the French adult population, were interviewed. Half received the incremental version, the other half the standard version. Results suggest that the incremental approach provides a ranking of options fully in line with explicit ranking. The standard approach was reasonably consistent with explicit ranking but proved unable to differentiate between the five most preferred providers, as predicted by theory. Our findings suggest that the incremental approach provides results which can be used in priority-setting contexts.

Incremental algorithm for finding principal curves

Principal curves are a non-linear generalisation of principal components. They are smooth curves that pass through the middle of a data set to provide a new representation of those data to make tasks, such as visualisation and dimensionality reduction easier and more accurate. The subspace constrained mean shift (SCMS) algorithm is a recently proposed technique to find principal curves. The algorithm assumes that the complete data set is available in advance and that new data points cannot be added to the data set during the process. The algorithm finds the points on the principal curves by using the complete data set. In this paper, the authors investigate the situation where the entire data set is not available in advance and instead are sampled sequentially. They propose an incremental version of the SCMS algorithm that trains using a sequence of observations. Simulation results show the effectiveness of the proposed algorithm to find a principal curve using a stream of observations.

An incremental version of the k-center problem on boundary of a convex polygon

This paper studies an incremental version of the k-center problem with centers constrained to lie on boundary of a convex polygon. In the incremental k-center problem we considered, we are given a set of n demand points inside a convex polygon, facilities are constrained to lie on its boundary. Our algorithm produces an incremental sequence of facility sets $$B_{1}\subseteq B_{2}\subseteq \cdots \subseteq B_{n}$$B1⊆B2⊆?⊆Bn, where each $$B_{k}$$Bk contains k facilities. Such an algorithm is called $$\alpha $$?-competitive, if for any k, the maximum of the ratio between the value of solution $$B_{k}$$Bk and the value of an optimal k-center solution is no more than $$\alpha $$?. We present a polynomial time incremental algorithm with a competitive ratio $$\frac{3\sqrt{3}}{2}$$332 and we also prove a lower bound of 2.

Local Community Mining on Distributed and Dynamic Networks From a Multiagent Perspective

Distributed and dynamic networks are ubiquitous in many real-world applications. Due to the huge-scale, decentralized, and dynamic characteristics, the global topological view is either too hard to obtain or even not available. So, most existing community detection methods working on the global view fail to handle such decentralized and dynamic large networks. In this paper, we propose a novel autonomy-oriented computing-based method for community mining (AOCCM) from the multiagent perspective in the distributed environment. In particular, AOCCM utilizes reactive agents to pick the neighborhood node with the largest structural similarity as the candidate node, and thus determine whether it should be added into local community based on the modularity gain. We further improve AOCCM to a more efficient incremental version named AOCCM-i for mining communities from dynamic networks. AOCCM and AOCCM-i can be easily expanded to detect both nonoverlapping and overlapping global community structures. Experimental results on real-life networks demonstrate that the proposed methods can reduce the computational cost by avoiding repeated structural similarity calculation and can still obtain the high-quality communities.

A New and Fast Implementation of Orthogonal LDA Algorithm and Its Incremental Extension

Linear discriminant analysis (LDA) is one of the most popular dimension reduction methods and has been widely used in many applications. In the last decades many LDA-based dimension reduction algorithms have been reported. Among these methods, orthogonal LDA (OLDA) is a famous one and several different implementations of OLDA have been proposed. In this paper, we propose a new and fast implementation of OLDA. Compared with the other OLDA implementations, our proposed implementation of OLDA is the fastest one when the dimensionality d is larger than the sample size n. Then, based on our proposed implementation of OLDA, we present an incremental OLDA algorithm which can accurately update the projection matrix of OLDA when new samples are added into the training set. The effectiveness of our proposed new OLDA algorithm and its incremental version are demonstrated by some real-world data sets.

Incremental Version of the Modern Syllogistic Method

Incremental Version of the Modern Syllogistic Method

Chemical data visualization and analysis with incremental generative topographic mapping: big data challenge.

This paper is devoted to the analysis and visualization in 2-dimensional space of large data sets of millions of compounds using the incremental version of generative topographic mapping (iGTM). The iGTM algorithm implemented in the in-house ISIDA-GTM program was applied to a database of more than 2 million compounds combining data sets of 36 chemicals suppliers and the NCI collection, encoded either by MOE descriptors or by MACCS keys. Taking advantage of the probabilistic nature of GTM, several approaches to data analysis were proposed. The chemical space coverage was evaluated using the normalized Shannon entropy. Different views of the data (property landscapes) were obtained by mapping various physical and chemical properties (molecular weight, aqueous solubility, LogP, etc.) onto the iGTM map. The superposition of these views helped to identify the regions in the chemical space populated by compounds with desirable physicochemical profiles and the suppliers providing them. The data sets similarity in the latent space was assessed by applying several metrics (Euclidean distance, Tanimoto and Bhattacharyya coefficients) to data probability distributions based on cumulated responsibility vectors. As a complementary approach, data sets were compared by considering them as individual objects on a meta-GTM map, built on cumulated responsibility vectors or property landscapes produced with iGTM. We believe that the iGTM methodology described in this article represents a fast and reliable way to analyze and visualize large chemical databases.

An improved incremental nonlinear dimensionality reduction for isometric data embedding

Manifold learning has become a hot issue in the field of machine learning and data mining. There are some algorithms proposed to extract the intrinsic characteristics of different type of high-dimensional data by performing nonlinear dimensionality reduction, such as ISOMAP, LLE and so on. Most of these algorithms operate in a batch mode and cannot be effectively applied when data are collected sequentially. In this paper, we proposed a new incremental version of ISOMAP which can use the previous computation results as much as possible and effectively update the low dimensional representation of data points as many new samples are accumulated. Experimental results on synthetic data as well as real world images demonstrate that our approaches can construct an accurate low-dimensional representation of the data in an efficient manner.

I3QL

An incremental computation updates its result based on a change to its input, which is often an order of magnitude faster than a recomputation from scratch. In particular, incrementalization can make expensive computations feasible for settings that require short feedback cycles, such as interactive systems, IDEs, or (soft) real-time systems. This paper presents i3QL, a general-purpose programming language for specifying incremental computations. i3QL provides a declarative SQL-like syntax and is based on incremental versions of operators from relational algebra, enriched with support for general recursion. We integrated i3QL into Scala as a library, which enables programmers to use regular Scala code for non-incremental subcomputations of an i3QL query and to easily integrate incremental computations into larger software projects. To improve performance, i3QL optimizes user-defined queries by applying algebraic laws and partial evaluation. We describe the design and implementation of i3QL and its optimizations, demonstrate its applicability, and evaluate its performance.

Online discriminative component analysis feature extraction from stream data with domain knowledge

In this paper, we introduce an incremental version of recently proposed constrained Linear Discriminant Analysis (LDA). In addition of application in constrained LDA problems, our algorithm which we call Online Discriminative Component Analysis (ODCA) is usable in standard incremental LDA problems. ODCA incrementally computes the solution of LDA with the time complexity lower than most incremental algorithm for LDA while keeps the accuracy of final result as close as possible to offline version. This is done using a special formulation for the scatter matrix updating along with Eigen-space calculation. By exploiting such formulation, the proposed algorithm made capable of updating the solution where a data point added or removed from the problem. It is also usable in problems where its data points have concept drift property. To show efficiency of proposed algorithm, its speed is compared to other existing incremental algorithms as order of complexity. In addition, the classification accuracy of our approach is experimentally compared to other algorithms.

Parallel incremental power mean SVM for the classification of large-scale image datasets

The amount of image data becomes larger and larger, both image size (due the higher resolution) and image number. It is estimated for personal use only, an average single user will take 100,000 images during his life. The growth of image data is illustrated by the dataset size, for example ImageNet benchmark dataset is made of more than 14 million images and more than 21,000 classes. This is very challenging for classification algorithms. They have to deal with time and space complexity and very imbalanced data when using SVM algorithms. We present extensions of Power Mean SVM to deal with such data. The first one is an incremental version to deal with the space complexity, the second one is a parallel version of the incremental version to deal with time complexity and the last one is the use of a balanced bagging algorithm for training binary classifiers to deal with imbalanced data. We evaluate our parallel incremental version of balanced bagging PmSVM on the 1,000 classes of ImageNet (ILSVRC 2010). The results show that our algorithm can be run on standard PC (with eg. 2 or 4 GB RAM); it is 255 times faster than the original version and 1,276 times faster than state-of-the-art linear classifier, LIBLINEAR with 80 cores.

Unsupervised images segmentation via incremental dictionary learning based sparse representation

In this paper we propose a novel Dictionary Learning and Sparse Representation-based Classifier (DLSRC) for image segmentation. In DLSRC, instances-based learning is adopted to find representative dictionaries that can sparsely code various classes of prototype samples in images. Then an incremental version of DLSRC, IDLSRC, is advanced for incremental learning of accumulating knowledge obtained from labeled data. The unsupervised clustering algorithm provides initial labeled samples, and then the labels of candidate samples are incrementally predicted by defining a consistency-enhanced evaluation function. Some experiments are taken on both the artificial texture images and real Synthetic Aperture Radar (SAR) images, to investigate the performance of DLSRC and IDLSRC. Some aspects including (1) the comparison of DLSRC with the Sparse Representation based Classifier (SRC) and some unsupervised clustering approaches, (2) the comparison of IDLSRC with DLSRC, are tested, and the results prove the superiority of our proposed method to its counterparts.

An enhanced version and an incremental learning version of visual-attention-imitation convex hull algorithm

This paper presents an enhanced version and an incremental learning version of the visual-attention-imitation convex hull algorithm reported in our latest paper in Liu et al. (2012) [3]. The enhanced algorithm merges the virtue of point comparison of the Graham scan algorithm into the visual-attention-imitation convex hull algorithm. In comparison with its previous edition, the proposed algorithm achieved a significant time saving. In view of machine learning, there are interesting situations where training data acquisition must take place over time. An incremental learning version is also proposed in this paper in order to compute convex hulls of point sets whose points are acquired over time. The incremental learning version reuses the prior results and computes the new convex hull without processing of previous points. Experimental results show that the incremental learning version is more flexible and more efficient for incremental learning tasks.

Incremental proxy re-encryption scheme for mobile cloud computing environment

Due to the limited computational capability of mobile devices, the research organization and academia are working on computationally secure schemes that have capability for offloading the computational intensive data access operations on the cloud/trusted entity for execution. Most of the existing security schemes, such as proxy re-encryption, manager-based re-encryption, and cloud-based re-encryption, are based on El-Gamal cryptosystem for offloading the computational intensive data access operation on the cloud/trusted entity. However, the resource hungry pairing-based cryptographic operations, such as encryption and decryption, are executed using the limited computational power of mobile device. Similarly, if the data owner wants to modify the encrypted file uploaded on the cloud storage, after modification the data owner must encrypt and upload the entire file on the cloud storage without considering the altered portion(s) of the file. In this paper, we have proposed an incremental version of proxy re-encryption scheme for improving the file modification operation and compared with the original version of the proxy re-encryption scheme on the basis of turnaround time, energy consumption, CPU utilization, and memory consumption while executing the security operations on mobile device. The incremental version of proxy re-encryption scheme shows significant improvement in results while performing file modification operations using limited processing capability of mobile devices.

Identification of Malicious Nodes in Peer-to-Peer Streaming: A Belief Propagation-Based Technique

Peer-to-peer streaming has witnessed a great success thanks to the possibility of aggregating resources from all participants. Nevertheless, performance of the entire system may be highly degraded due to the presence of malicious peers that share bogus data on purpose. In this paper, we propose to use a statistical inference technique, namely, belief propagation (BP), to estimate the probability of peers being malicious. The detection algorithm is run by a set of trusted monitor nodes that receives notification messages (checks) from peers whenever they obtain a chunk of data; these checks contain the list of the chunk uploaders and a flag to mark the chunk as polluted or clean. Peers are able to detect if the received chunk is polluted or not but, since multiparty download is employed, they are not capable to identify the source(s) of bogus blocks. This problem definition allows us to define a factor graph of peers and checks on which an incremental version of the belief propagation algorithm is run by the monitor nodes to infer the probability of each peer being a malicious one. We evaluate the accuracy, robustness, and complexity of our technique by running a real peer-to-peer application on PlanetLab. We show that the proposed approach is very accurate and robust against malicious nodes misbehaving (different pollution intensity, presence of fake checks, churning, and total uncooperation from malicious nodes), increasing number and colluding behavior of malicious nodes.

Incremental ECAP with Converging Billets

Abstract: A new concept of incremental equal channel angular pressing (I-ECAP) with converging billets is proposed and simulated numerically. It follows a recently introduced process of ECAP with converging billets, in which the contact surface between converging billets plays the same role as a movable die wall in the output channel of classical ECAP and thus reduces friction and the process force. The process productivity is doubled and material pickup, especially problematic in the output channel, avoided. However, ECAP with converging billets, as any ECAP-based process, suffers from a limited length of the billets it can process. This paper proposes an incremental version of ECAP with converging billets, which enables processing very long billets. Additionally, a new option for ECAP or I-ECAP with converging billets is considered, which assumes their separation with a movable tool. This tool can also be used to apply a back force. FEM simulations of all these processes enable their comparison in terms of strain distribution and the force required.

Incremental Planning with Adaptive Dimensionality

Path planning is often a high-dimensional computationally-expensive planning problem as it requires reasoning about the kinodynamic constraints of the robot and collisions of the robot with the environment. However, large regions of the environment are typically benign enough that a much faster low-dimensional planning combined with a local path following controller suffice. Planning with Adaptive Dimensionality that was recently developed makes use of this observation and iteratively constructs and searches a state-space consisting of mainly low-dimensional states. It only introduces regions of high-dimensional states into the state-space where they are necessary to ensure completeness and bounds on sub-optimality. However, due to its iterative nature, the approach relies on running a series of weighted A* searches. In this paper, we introduce and apply to Planning with Adaptive Dimensionality a simple but very effective incremental version of weighted A* that reuses its previously generated search tree if available. On the theoretical side, the new algorithm preserves guarantees on completeness and bounds on sub-optimality. On the experimental side, it speeds up 3D (x,y,heading) path planning with a full-body collision checking by up to a factor of 5. Our results also show that it tends to be much faster than applying alternative incremental graph search techniques such as D* to Planning with Adaptive Dimensionality.

Model-based clustering of high-dimensional data streams with online mixture of probabilistic PCA

Model-based clustering is a popular tool which is renowned for its probabilistic foundations and its flexibility. However, model-based clustering techniques usually perform poorly when dealing with high-dimensional data streams, which are nowadays a frequent data type. To overcome this limitation of model-based clustering, we propose an online inference algorithm for the mixture of probabilistic PCA model. The proposed algorithm relies on an EM-based procedure and on a probabilistic and incremental version of PCA. Model selection is also considered in the online setting through parallel computing. Numerical experiments on simulated and real data demonstrate the effectiveness of our approach and compare it to state-of-the-art online EM-based algorithms.

An Incremental DPMM-Based Method for Trajectory Clustering, Modeling, and Retrieval

Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.

Range-Based Skyline Queries in Mobile Environments

Skyline query processing for location-based services, which considers both spatial and nonspatial attributes of the objects being queried, has recently received increasing attention. Existing solutions focus on solving point- or line-based skyline queries, in which the query location is an exact location point or a line segment. However, due to privacy concerns and limited precision of localization devices, the input of a user location is often a spatial range. This paper studies a new problem of how to process such range-based skyline queries. Two novel algorithms are proposed: one is index-based (I-SKY) and the other is not based on any index (N-SKY). To handle frequent movements of the objects being queried, we also propose incremental versions of I-SKY and N-SKY, which avoid recomputing the query index and results from scratch. Additionally, we develop efficient solutions for probabilistic and continuous range-based skyline queries. Experimental results show that our proposed algorithms well outperform the baseline algorithm that adopts the existing line-based skyline solution. Moreover, the incremental versions of I-SKY and N-SKY save substantial computation cost, especially when the objects move frequently.