Query: YouTube2Text: Recognizing and Describing Arbitrary Activities Using Semantic Hierarchies and Zero-Shot Recognition

Relevant passages:
Distributional Clustering of English Words <=SEP=> We describe and experimentally evaluate a method for automatically clustering words according to their distribution in particular syntactic contexts. Deterministic annealing is used to find lowest distortion sets of clusters. As the annealing parameter increases, existing clusters become unstable and subdivide, yielding a hierarchical “soft” clustering of the data. Clusters are used as the basis for class models of word coocurrence, and the models evaluated with respect to held-out test data.

Verb Semantics and Lexical Selection <=SEP=> This paper will focus on the semantic representation of verbs in computer systems and its impact on lexical selection problems in machine translation (MT). Two groups of English and Chinese verbs are examined to show that lexical selection must be based on interpretation of the sentence as well as selection restrictions placed on the verb arguments. A novel representation scheme is suggested, and is compared to representations with selection restrictions used in transfer-based MT. We see our approach as closely aligned with knowledge-based MT approaches (KBMT), and as a separate component that could be incorporated into existing systems. Examples and experimental results will show that, using this scheme, inexact matches can achieve correct lexical selection.

Hedging your bets: Optimizing accuracy-specificity trade-offs in large scale visual recognition <=SEP=> As visual recognition scales up to ever larger numbers of categories, maintaining high accuracy is increasingly difficult. In this work, we study the problem of optimizing accuracy-specificity trade-offs in large scale recognition, motivated by the observation that object categories form a semantic hierarchy consisting of many levels of abstraction. A classifier can select the appropriate level, trading off specificity for accuracy in case of uncertainty. By optimizing this trade-off, we obtain classifiers that try to be as specific as possible while guaranteeing an arbitrarily high accuracy. We formulate the problem as maximizing information gain while ensuring a fixed, arbitrarily small error rate with a semantic hierarchy. We propose the Dual Accuracy Reward Trade-off Search (DARTS) algorithm and prove that, under practical conditions, it converges to an optimal solution. Experiments demonstrate the effectiveness of our algorithm on datasets ranging from 65 to over 10,000 categories.

Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods <=SEP=> 

Corpus-Guided Sentence Generation of Natural Images <=SEP=> We propose a sentence generation strategy that describes images by predicting the most likely nouns, verbs, scenes and prepositions that make up the core sentence structure. The input are initial noisy estimates of the objects and scenes detected in the image using state of the art trained detectors. As predicting actions from still images directly is unreliable, we use a language model trained from the English Gigaword corpus to obtain their estimates; together with probabilities of co-located nouns, scenes and prepositions. We use these estimates as parameters on a HMM that models the sentence generation process, with hidden nodes as sentence components and image detections as the emissions. Experimental results show that our strategy of combining vision and language produces readable and descriptive sentences compared to naive strategies that use vision alone.

Understanding user situational relevance in ranking web search results <=SEP=> Relevance plays a key role in determining how ranking features are weighed in rank algorithms. In particular, the Search Engine Result Page (SERP) presents documents from the algorithmic relevance perspective. User relevance, however, is situational and describes the searcher's perception of the contextual document which is interpreted at rank time. Therefore, without it the monotonic decreasing click trend portrayed by rank algorithms will not hold for real time searchers. Instead, users select low ranked documents over high ones or reject the ranking, leading to frustration and several re-query tasks. To understand users situational relevance (USR), we formulate a decreasing probability function to measure each user's personalized probabilistic relevance (PPR). Next, we find an aggregated PPR representing the USR across several query categories. Then, we apply maximum probabilities swapping to modify the rank function and re-rank documents. Finally, we use Normalized Discounted Cumulative Gain (NDCG) and click weight metrics for evaluation. Our results show that a clear interpretation and incorporation of user situational relevance improves click satisfaction by 12.86%, and optimizes click through rate up to 42%.

3D reconstruction based on SIFT and Harris feature points <=SEP=> This paper presents a new 3D reconstruction method using feature points extracted by the SIFT and Harris corner detector. Since the SIFT feature points can be detected stably and relatively accurately, the proposed algorithm first uses the SIFT matching points to calculate the fundamental matrix. On the other hand many of the feature points detected by the SIFT are not what we need for reconstruction, so by combining the SIFT feature points with the Harris corners it is possible to obtain more vivid and detailed 3D information. Experiments have been conducted to validate the proposed method.

GaN-based high frequency totem-pole bridgeless PFC design with digital implementation <=SEP=> Totem-pole bridgeless PFC is a very promising topology for GaN devices because of very low reverse recovery. However, inherent challenges exist for this topology at zerocrossing point of AC voltage where inductor, current spike is observed which may cause higher harmonics and EMI issue. This paper looks deeply into this phenomena and found two main causes: the transition delay of low frequency leg and the minimum pulse width limit of high frequency leg. A DSP-based digital modulator is designed to address those two issues by applying lead time to the low frequency leg and digital dithering to increase equivalent resolution at the high frequency leg. Some other rules are also summarized to enable robust modulator design, which is the main challenge in this topology. A 500 kHz GaN totem-pole bridgeless PFC converter operating in continuous conduction mode verified the effectiveness of those methods to eliminate the current spike at zero-crossing. All those findings will help the implementation of wide band gap devices in very high frequency PFC circuit.

Vishleshan: Performance Comparison and Programming Process Mining Algorithms in Graph-Oriented and Relational Database Query Languages <=SEP=> Process-Aware Information System (PAIS) are IT systems that manages, supports business processes and generate large event logs from execution of business processes. Process Mining consists of analyzing event logs generated by PAISs and discover business process models and check for conformance between the discovered and actual models. The large volume of event logs generated are stored in databases. Relational databases perform well for certain class of applications. However, there are certain class of applications for which relational databases are not able to scale. Several NoSQL databases have emerged to encounter the challenges of scalability in traditional databases. Discovering social network from event logs is one of the most challenging and important Process Mining task. Similar-Task algorithm is one of the most widely used Organizational Mining techniques. Our objective is to investigate which of the databases (Relational or Graph) perform better for Organizational Mining under Process Mining. We implement Similar-Task algorithm on relational and NoSQL (graph oriented) databases using only query language constructs. We conduct empirical analysis on a large real world data set to compare the performance of row-oriented database and NoSQL graph-oriented database.

Knowledge management infrastructure and processes on effectiveness of nursing care <=SEP=> The purpose of this study is to investigate the influence of knowledge management infrastructure and process on nursing care effectiveness in selected teaching hospitals in Southwest Nigeria. The organization of nursing knowledge resources is critical to health care organizations for providing safe and high quality care for patients. Therefore, it is necessary to study the role of knowledge management in clinical nursing practice and the outcomes on the effectiveness of nursing care. Despite the critical role of nursing care in defining high-performing health care delivery, knowledge management in this area is still at an early stage of development. Based on organizational capability theory and employing concurrent mixed-method research design, this study seeks to identify the components of knowledge infrastructure and processes that influence the efficiency and effectiveness of nursing care in clinical floor nursing of selected teaching hospitals in Nigeria, a developing country in Africa. The output from the research is expected to contribute to the domain of literature/knowledge; provide awareness of the knowledge management practices in nursing care in Nigeria; assist nursing administrators, health policy makers and hospital management to exploit and make effective use of knowledge-based resources to enhance nursing care which can improve the productivity of health care organizations.

Bidirectional bridgeless PFC with reduced input current distortion and switching loss using gate skipping technique <=SEP=> Bidirectional bridgeless PFC is an advantageous topology for high power application among many bridgeless PFCs due to small EMI filter size and no reverse recovery problem. However due to the large junction capacitance of secondary leg's diodes, the dead angle is shown in the zero-crossing area. Thus, the input current distortion is intensified, and the unnecessary switching loss decreases the efficiency. In this paper, a simple gate skipping technique is implemented to the zero-crossing area of the bidirectional bridgeless PFC. As a result, the unnecessary switching loss of boost switches and the input current distortion are reduced. The improvement of the proposed method is verified by experimental results with the high line input and 800W(400V/2A) output prototype.

Patient Monitoring System Using GSM Technology <=SEP=> In this fast pace of life, it is difficult for people to be constantly available for their near ones who might need them while they are suffering from a disease or physical disorder. So also constant monitoring of the patient’s body parameters such as temperature, pulse rate, sugar level etc. becomes difficult. Hence to remove human error and to lessen the burden of monitoring patient’s health from doctor’s head, this paper presents the methodology for monitoring patients remotely using GSM network and Very Large Scale Integration (VLSI) technology. Patient monitoring systems measure physiological characteristics either continuously or at regular intervals of time.

AI Challenges in Human-Robot Cognitive Teaming <=SEP=> Among the many anticipated roles for robots in the future is that of being a human teammate. Aside from all the technological hurdles that have to be overcome with respect to hardware and control to make robots fit to work with humans, the added complication here is that humans have many conscious and subconscious expectations of their teammates – indeed, we argue that teaming is mostly a cognitive rather than physical coordination activity. This introduces new challenges for the AI and robotics community and requires fundamental changes to the traditional approach to the design of autonomy. With this in mind, we propose an update to the classical view of the intelligent agent architecture, highlighting the requirements for mental modeling of the human in the deliberative process of the autonomous agent. In this article, we outline briefly the recent efforts of ours, and others in the community, towards developing cognitive teammates along these guidelines.

Anisotropic diffusion map based spectral embedding for 3D CAD model retrieval <=SEP=> In the product life cycle, design reuse can save cost and improve existing products conveniently in most new product development. To retrieve similar models from big database, most search algorithms convert CAD model into a shape descriptor and compute the similarity two models according to a descriptor metric. This paper proposes a new 3D shape matching approach by matching the coordinates directly. It is based on diffusion maps which integrate the rand walk and graph spectral analysis to extract shape features embedded in low dimensional spaces and then they are used to form coordinations for non-linear alignment of different models. These coordinates could capture multi-scale properties of the 3D geometric features and has shown good robustness to noise. The results also have shown better performance compared to the celebrated Eigenmap approach in the 3D model retrieval.

Bystander Responses to a Violent Incident in an Immersive Virtual Environment <=SEP=> Under what conditions will a bystander intervene to try to stop a violent attack by one person on another? It is generally believed that the greater the size of the crowd of bystanders, the less the chance that any of them will intervene. A complementary model is that social identity is critical as an explanatory variable. For example, when the bystander shares common social identity with the victim the probability of intervention is enhanced, other things being equal. However, it is generally not possible to study such hypotheses experimentally for practical and ethical reasons. Here we show that an experiment that depicts a violent incident at life-size in immersive virtual reality lends support to the social identity explanation. 40 male supporters of Arsenal Football Club in England were recruited for a two-factor between-groups experiment: the victim was either an Arsenal supporter or not (in-group/out-group), and looked towards the participant for help or not during the confrontation. The response variables were the numbers of verbal and physical interventions by the participant during the violent argument. The number of physical interventions had a significantly greater mean in the in-group condition compared to the out-group. The more that participants perceived that the Victim was looking to them for help the greater the number of interventions in the in-group but not in the out-group. These results are supported by standard statistical analysis of variance, with more detailed findings obtained by a symbolic regression procedure based on genetic programming. Verbal interventions made during their experience, and analysis of post-experiment interview data suggest that in-group members were more prone to confrontational intervention compared to the out-group who were more prone to make statements to try to diffuse the situation.

Bayesian policy reuse <=SEP=> A long-lived autonomous agent should be able to respond online to novel instances of tasks from a familiar domain. Acting online requires ‘fast’ responses, in terms of rapid convergence, especially when the task instance has a short duration such as in applications involving interactions with humans. These requirements can be problematic for many established methods for learning to act. In domains where the agent knows that the task instance is drawn from a family of related tasks, albeit without access to the label of any given instance, it can choose to act through a process of policy reuse from a library in contrast to policy learning. In policy reuse, the agent has prior experience from the class of tasks in the form of a library of policies that were learnt from sample task instances during an offline training phase. We formalise the problem of policy reuse and present an algorithm for efficiently responding to a novel task instance by reusing a policy from this library of existing policies, where the choice is based on observed ‘signals’ which correlate to policy performance. We achieve this by posing the problem as a Bayesian choice problem with a corresponding notion of an optimal response, but the computation of that response is in many cases intractable. Therefore, to reduce the computation cost of the posterior, we follow a Bayesian optimisation approach and define a set of policy selection functions, which balance exploration in the policy library against exploitation of previously tried policies, together with a model of expected performance of the policy library on their corresponding task instances. We validate our method in several simulated domains of interactive, short-duration episodic tasks, showing rapid convergence in unknown task variations.

BotGuard: Lightweight real-time botnet detection in software defined networks <=SEP=> The distributed detection of botnets may induce heavy computation and communication costs to network devices. Each device in related scheme only has a regional view of Internet, so it is hard to detect botnet comprehensively. In this paper, we propose a lightweight real-time botnet detection framework called Bot-Guard, which uses the global landscape and flexible configurability of software defined network (SDN) to identify botnets promptly. SDN, as a new network framework, can make centralized control in botnet detection, but there are still some challenges in such detections. We give a convex lens imaging graph (CLI-graph) to depict the topology characteristics of botnet, which allows SDN controller to locate attacks separately and mitigate the burden of network devices. The theoretical and experimental results prove that our scheme is capable of timely botnet detecting in SDNs with the accuracy higher than 90% and the delay less than 56 ms.

A Traceback Attack on Freenet <=SEP=> Freenet is a popular peer to peer anonymous content-sharing network, with the objective to provide the anonymity of both content publishers and retrievers. Despite more than a decade of active development and deployment and the adoption of well-established cryptographic algorithms in Freenet, it remains unanswered how well the anonymity objective of the initial Freenet design has been met. In this paper we develop a traceback attack on Freenet, and show that the originating machine of a content request message in Freenet can be identified; that is, the anonymity of a content retriever can be broken, even if a single request message has been issued by the retriever. We present the design of the traceback attack, and perform both experimental and simulation studies to confirm the feasibility and effectiveness of the attack. For example, with randomly chosen content requesters (and random contents stored in the Freenet testbed), Emulab-based experiments show that, for <inline-formula><tex-math notation="LaTeX">$24$</tex-math> <alternatives><inline-graphic xlink:href="duan-ieq1-2453983.gif"/></alternatives></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$43$</tex-math><alternatives><inline-graphic xlink:href="duan-ieq2-2453983.gif"/> </alternatives></inline-formula> percent of the content request messages, we can identify their originating machines. We also briefly discuss potential solutions to address the developed traceback attack. Despite being developed specifically on Freenet, the basic principles of the traceback attack and solutions have important security implications for similar anonymous content-sharing systems.

Data summaries for on-demand queries over linked data <=SEP=> Typical approaches for querying structured Web Data collect (crawl) and pre-process (index) large amounts of data in a central data repository before allowing for query answering. However, this time-consuming pre-processing phase however leverages the benefits of Linked Data -- where structured data is accessible live and up-to-date at distributed Web resources that may change constantly -- only to a limited degree, as query results can never be current. An ideal query answering system for Linked Data should return current answers in a reasonable amount of time, even on corpora as large as the Web. Query processors evaluating queries directly on the live sources require knowledge of the contents of data sources. In this paper, we develop and evaluate an approximate index structure summarising graph-structured content of sources adhering to Linked Data principles, provide an algorithm for answering conjunctive queries over Linked Data on theWeb exploiting the source summary, and evaluate the system using synthetically generated queries. The experimental results show that our lightweight index structure enables complete and up-to-date query results over Linked Data, while keeping the overhead for querying low and providing a satisfying source ranking at no additional cost.

Pure Pursuit Revisited: Field Testing of Autonomous Vehicles in Urban Areas <=SEP=> In this paper, we aim to explore path following. We implement a path following component by referring to the existing Pure Pursuit algorithm. Using the simulation and field operational test, we identified the problem in the path following component. The main problems identified were with respect to vehicles meandering off the path, turning a corner, and the instability of steering control. Therefore, we apply some modifications to the Pure Pursuit[1] algorithm. We have also conducted the simulation and field operational tests again to evaluate these modifications.

An ontological analysis of the economic primitives of the extended-REA enterprise information architecture <=SEP=> The Resource-Event-Agent (REA) model for enterprise economic phenomena was first published in The Accounting Review in 1982. Since that time its concepts and use have been extended far beyond its original accountability infrastructure to a framework for enterprise information architectures. The granularity of the model has been extended both up (to enterprise value chains) and down (to workflow or task specification) in the aggregation plane, and additional conceptual type-images and commitment-images have been proposed as well. The REA model actually fits the notion of domain ontology well, a notion that is becoming increasingly important in the era of e-commerce and virtual companies. However, its present and future components have never been analyzed formally from an ontological perspective. This paper intends to do just that, relying primarily on the conceptual terminology of John Sowa. The economic primitives of the original REA model (economic resources, economic events, economic agents, duality relationships, stock-flow relationships, control relationships and responsibility relationships) plus some newer components (commitments, types, custody, reciprocal, etc.) will be analyzed individually and collectively as a specific domain ontology. Such a review can be used to guide further conceptual development of REA extensions.

Point-of-Interest Demand Modeling with Human Mobility Patterns <=SEP=> Point-of-Interest (POI) demand modeling in urban regions is critical for many applications such as business site selection and real estate investment. While some efforts have been made for the demand analysis of some specific POI categories, such as restaurants, it lacks systematic means to support POI demand modeling. To this end, in this paper, we develop a systematic POI demand modeling framework, named Region POI Demand Identification (RPDI), to model POI demands by exploiting the daily needs of people identified from their large-scale mobility data. Specifically, we first partition the urban space into spatially differentiated neighborhood regions formed by many small local communities. Then, the daily activity patterns of people traveling in the city will be extracted from human mobility data. Since the trip activities, even aggregated, are sparse and insufficient to directly identify the POI demands, especially for underdeveloped regions, we develop a latent factor model that integrates human mobility data, POI profiles, and demographic data to robustly model the POI demand of urban regions in a holistic way. In this model, POI preferences and supplies are used together with demographic features to estimate the POI demands simultaneously for all the urban regions interconnected in the city. Moreover, we also design efficient algorithms to optimize the latent model for large-scale data. Finally, experimental results on real-world data in New York City (NYC) show that our method is effective for identifying POI demands for different regions.

Credit Card Fraud Detection: A case study <=SEP=> In this research, a technique for `Credit Card Fraud Detection' is developed. As fraudsters are increasing day by day. And fallacious transactions are done by the credit card and there are various types of fraud. So to solve this problem combination of technique is used like Genetic Algorithm, Behavior Based Technique and Hidden Markov Model. By this transaction is tested individually and whatever suits the best is further proceeded. And the foremost goal is to detect fraud by filtering the above techniques to get better result.

A Review on the ESD Robustness of Drain-Extended MOS Devices <=SEP=> This paper reviews electrostatic discharge (ESD) investigations on laterally diffused MOS (LDMOS) and drain-extended MOS (DeMOS) devices. The limits of the safe operating area of LDMOS/DeMOS devices and device physics under ESD stress are discussed under various biasing conditions and layout schemes. Specifically, the root cause of early filament formation is highlighted. Differences in filamentary nature among various LDMOS/DeMOS devices are shown. Based on the physical understanding, device optimization guidelines are given. Finally, an outlook on technology scaling is presented.

A Survey on Cryptography and Steganography Methods for Information Security <=SEP=> This paper deals with the tidings of cryptography in history, how it has played a vital role in World War -1, World War-2. It also deals with the Substitution, Transposition Cryptographic techniques and Steganography principles which can be used to maintain the confidentiality of computerized and none computerized information files. A number of well known techniques have been adapted for computer usage including the Ceaser cipher, Mono alphabetic cipher, Homophonic substitution, Bale cipher, Play fair cipher, Poly alphabetic cipher, Vigenere Cipher, Onetime pad cipher, Vernam ciphers, Play Color Cipher and usage of rotor machine in Substitutions, Rail fence technique, more complex permutations for more secure transposition and some Steganography principle were briefly discussed with merits and demerits. Finally it gives the broad knowledge on almost all the cryptographic and Steganography principles where a reader or scholar have lot of scope for updating or invention of more secure algorithms to fulfill the global needs in information security.

Human C-tactile afferents are tuned to the temperature of a skin-stroking caress. <=SEP=> Human C-tactile (CT) afferents respond vigorously to gentle skin stroking and have gained attention for their importance in social touch. Pharmacogenetic activation of the mouse CT equivalent has positively reinforcing, anxiolytic effects, suggesting a role in grooming and affiliative behavior. We recorded from single CT axons in human participants, using the technique of microneurography, and stimulated a unit's receptive field using a novel, computer-controlled moving probe, which stroked the skin of the forearm over five velocities (0.3, 1, 3, 10, and 30 cm s(-1)) at three temperatures (cool, 18 °C; neutral, 32 °C; warm, 42 °C). We show that CTs are unique among mechanoreceptive afferents: they discharged preferentially to slowly moving stimuli at a neutral (typical skin) temperature, rather than at the cooler or warmer stimulus temperatures. In contrast, myelinated hair mechanoreceptive afferents proportionally increased their firing frequency with stroking velocity and showed no temperature modulation. Furthermore, the CT firing frequency correlated with hedonic ratings to the same mechano-thermal stimulus only at the neutral stimulus temperature, where the stimuli were felt as pleasant at higher firing rates. We conclude that CT afferents are tuned to respond to tactile stimuli with the specific characteristics of a gentle caress delivered at typical skin temperature. This provides a peripheral mechanism for signaling pleasant skin-to-skin contact in humans, which promotes interpersonal touch and affiliative behavior.

Design and fabrication of an autonomous fire fighting robot with multisensor fire detection using PID controller <=SEP=> Recently, Multisensor Fire Detection System (MSFDS) is one of the important research issues. Here, a fire fighter robot is fabricated providing extinguishment platform. The base of the robot is made of the wood of `Rashed tree', locally known as `Kerosene wood'. There is about 1 liter water reserving capacity. An arduino based simple algorithm is used for detection of fire and measurement of distance from fire source while the robot is on its way to extinguish fire. When the fire is detected and the robot is at a distance near to fire, a centrifugal pump is used to throw water for extinguishment purpose. A water spreader is used for effective extinguishing. It is seen that velocity of water is greatly reduced due to the use of water spreader. Two sensors: LM35 and Arduino Flame Sensors are used to detect the fire and distances on its way towards fire. Sensitivity of these sensors at different day times and distances is tested through analog reading of the serial monitor.

Towards Discipline-Independent Argumentative Zoning : Evidence from Chemistry and Computational Linguistics <=SEP=> Argumentative Zoning (AZ) is an analysis of the argumentative and rhetorical structure of a scientific paper. It has been shown to be reliably used by independent human coders, and has proven useful for various information access tasks. Annotation experiments have however so far been restricted to one discipline, computational linguistics (CL). Here, we present a more informative AZ scheme with 15 categories in place of the original 7, and show that it can be applied to the life sciences as well as to CL. We use a domain expert to encode basic knowledge about the subject (such as terminology and domain specific rules for individual categories) as part of the annotation guidelines. Our results show that non-expert human coders can then use these guidelines to reliably annotate this scheme in two domains, chemistry and computational linguistics.

Fitting conic sections to "very scattered" data: An iterative refinement of the bookstein algorithm <=SEP=> An examination of the geometric interpretation of the error-of-fit measure of the Bookstein algorithm for fitting conic sections shows why it may not be entirely satisfactory when the data are “very scattered” in the sense that the data points are distributed rather widely about an underlying smooth curve. A simple iterative refinement of the Bookstein algorithm, similar in spirit to iterative weighted least-squares methods in regression analysis, results in a fitted conic section that approximates the conic that would minimize the sum of squared orthogonal distances of data points from the fitted conic. The usefulness and limitations of the refined algorithm are demonstrated on two different types of “very scattered” data.

Walking on Minimax Paths for k-NN Search <=SEP=> Link-based dissimilarity measures, such as shortest path or Euclidean commute time distance, base their distance on paths between nodes of a weighted graph. These measures are known to be better suited to data manifold with nonconvex-shaped clusters, compared to Euclidean distance, so that k-nearest neighbor (NN) search is improved in such metric spaces. In this paper we present a new link-based dissimilarity measure based on minimax paths between nodes. Two main benefits of minimax path-based dissimilarity measure are: (1) only a subset of paths is considered to make it scalable, while Euclidean commute time distance considers all possible paths; (2) it better captures nonconvex-shaped cluster structure, compared to shortest path distance. We define the total cost assigned to a path between nodes as Lp norm of intermediate costs of edges involving the path, showing that minimax path emerges from our Lp norm over paths framework. We also define minimax distance as the intermediate cost of the longest edge on the minimax path, then present a greedy algorithm to compute k smallest minimax distances between a query and N data points in O(logN + k log k) time. Numerical experiments demonstrate that our minimax kNN algorithm reduce the search time by several orders of magnitude, compared to existing methods, while the quality of k-NN search is significantly improved over Euclidean distance. Introduction Given a set of N data points X = {x1, . . . ,xN}, k-nearest neighbor (k-NN) search in metric spaces involves finding k closest points in the dataset X to a query xq . Dissimilarity measure defines distance duv between two data points (or nodes of a weighted graph) xu and xv in the corresponding metric space, and the performance of k-NN search depends on distance metric. Euclidean distance ‖xu − xv‖2 is the most popular measure for k-NN search but it does not work well when data points X lie on a curved manifold with nonconvex-shaped clusters (see Fig. 1(a)). Metric learning (Xing et al. 2003; Goldberger et al. 2005; Weinberger and Saul 2009) optimizes parameters involving the Mahalanobis distance using Copyright c © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. labeled dataset, such that points in the same cluster become close together and points in the different cluster become far apart. Most of metric learning methods are limited to linear embedding, so that the nonconvex-shaped cluster structure is not well captured (see Fig. 1(b)). Link-based (dis)similarity measures (Fouss et al. 2007; Yen, Mantrach, and Shimbo 2008; Yen et al. 2009; Mantrach et al. 2010; Chebotarev 2011) rely on paths between nodes of a weighted graph, on which nodes are associated with data points and intermediate costs (for instance Euclidean distance) are assigned to edge weights. Distance between nodes depends on the total cost that is computed by aggregating edge weights on a path connecting those nodes of interest. Total cost associated with a path is often assumed to be additive (Yen, Mantrach, and Shimbo 2008), so the aggregation reduces to summation. Dissimilarity between two nodes is calculated by integrating the total costs assigned to all possible paths between them. Such integration is often determined by computing the pseudo-inverse of the graph Laplacian, leading to Euclidean commute time distance (ECTD), regularized Laplacian kernel, and Markov diffusion kernel (see (Fouss et al. 2007; Yen, Mantrach, and Shimbo 2008; Fouss et al. 2012) and references therein), which are known to better capture the nonconvex-shaped cluster structure (see Fig. 1(c)). However, all possible paths between nodes need to be considered to compute the distances and the inversion of N × N matrix requires O(N) time and O(N) space, so it does not scale well to the problems of interest. Shortest path distance (Dijkstra 1959) is also a popular link-based dissimilarity measure (Tenenbaum, de Silva, and Langford 2000), where only shortest paths are considered to compute the distances between two nodes. Computational cost is reduced, but the cluster structure is not well captured when shortest path distance is used for k-NN search (see Fig. 1(d)). The distance between two nodes is computed along the shortest path only, Randomized shortest path (RSP) dissimilarity measures were proposed as a family of distance measures depending on a single parameter, which has interesting property of reducing, on one end, to the standard shortest path distance when the parameter is large, on the other hand, to the commute time distance when the parameter is near zero (Yen, Mantrach, and Shimbo 2008). In this paper we present a new link-based k-NN search method with minimax paths (Pollack 1960; Gower and Ross (a) Euclidean (b) LMNN (c) ECTD (d) Shortest path (e) Minimax distance Figure 1: Real-world datasets usually contain nonconvex-shaped clusters. For example, two curved clusters (dots) and a query point (star) are given. Heat map shows the closeness to the query at each point, in terms of the distance measure used (best viewed in color; more reddish means closer to the query). (a) Euclidean distance cannot reflect the underlying cluster structure at all. (b) Metric learning (Weinberger and Saul 2009) cannot capture the nonconvex shapes. (c) Link-based distance (Fouss et al. 2007) captures the underlying cluster correctly, but requires a large amount of computation. (d) Shortest path distance is efficient to compute, but it is not reliable near the boundary between different clusters. (e) Our method is as efficient as computing the shortest path distance, while capturing the entire cluster structure correctly. 1969), which is well-known to capture the underlying cluster structure of data (e.g., Fig. 1(e)) (Kim and Choi 2007; Luo et al. 2008; Zadeh and Ben-David 2009). We develop a fast k-NN search algorithm that computes the minimax distance efficiently through the minimax paths between data points. Our method is as efficient as the shortest-path distance computation. More specifically, the overall time for kNN search with N data points is onlyO(logN+k log k). In image retrieval experiments on some public image datasets, our method was several orders of magnitude faster, while achieving the comparable search quality (in terms of precision). The main contributions of this paper are summarized: 1. We develop a novel framework for link-based (dis)similarity measures where we define the total cost (corresponding to dissimilarity) assigned to a path between nodes as Lp norm of intermediate costs of edges involving the path, showing that minimax path emerges from our Lp norm over paths framework. This framework has two main benefits: (1) it can reduce the number of paths considered for the link-based (dis)similarity computation, which improves the scalability greatly; (2) even using a small number of paths, it can capture any nonconvex-shaped cluster structure successfully. 2. We define minimax distance as the intermediate cost of the longest edge on the minimax path, then present a greedy algorithm to compute k smallest minimax distances between a query and N data points in O(logN + k log k) time, whereas the state of the arts, minimax message passing (Kim and Choi 2007) requires O(N) time for k-NN search. Aggregation over Paths: Lp Norm Link-based (dis)similarity is defined on a weighted graph, denoted by G = (X , E): • The set of nodes, X = {x1, . . . ,xN}, are associated with N data points. E = {(i, j) | i 6= j ∈ {1, . . . , N}} is a set of edges between the nodes, excluding self-loop edges. • The graph is usually assumed to be sparse such that the number of edges, |E|, is limited to O(N). A well-known example is the K-NN graph, where an edge (i, j) exists only if xi is one of the K nearest neighbors of xj or xj is one of the K nearest neighbors of xi in the Euclidean space. The value of K is set to a small constant (usually 5-20) for ensuring sparsity. Then a link-based (dis)similarity depends on the total cost associated with paths on the graph. In this section, we describe our Lp norm approach to the cost aggregation over paths to compute the total cost along paths. Let a = (a0, a1, . . . , am) be a path with m hops, connecting the nodes xa0 ,xa1 , . . . ,xam ∈ X through the consecutive edges {(a0, a1), (a1, a2), . . . , (am−1, am)} ⊆ E . We denote a set of paths with m hops between an initial node xu and a destination node xv by Auv = { a ∣∣ a0 = u, am = v, (a`, a`+1) ∈ E , a` 6= v, ∀` = 0, . . . ,m− 1 } , (1) and denote a set of all possible paths between xu and xv by Auv = ∪m=1Auv. (2) A weight c(i, j) is assigned to each edge (i, j), representing the intermediate cost of following edge (i, j), usually defined as the Euclidean distance, i.e., c(i, j) = ‖xi − xj‖. We define the total cost associated with a path a as