T. Ravindra Babu

Rapid and brief communication: Classification of run-length encoded binary data

In classification of binary featured data, distance computation is carried out by considering each feature. We represent the given binary data as run-length encoded data. This would lead to a compact or compressed representation of data. Further, we propose an algorithm to directly compute the Manhattan distance between two such binary encoded patterns. We show that classification of data in such compressed form would improve the computation time by a factor of 5 on large handwritten data. The scheme is useful in large data clustering and classification which depend on distance measures.

Hybrid learning scheme for data mining applications

Classification of large datasets is a challenging task in data mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach integrating the activities of data abstraction, frequent item generation, compression, classification and use of rough sets.

Multiagent Systems for Large Data Clustering

Multiagent system is an applied research area encompassing many disciplines. With increasing computing power and easy availability of storage devices vast volumes of data is available containing enormous amount of hidden information. Generating abstractions from such large data is a challenging data mining task. Efficient large data clustering schemes are important in dealing with such large data. In the current work we provide two different efficient approaches of multiagent based large pattern clustering that would generate abstraction with single database scan, integrating domain knowledge, multiagent systems, data mining and intelligence through agent-mining interaction. We illustrate the approaches based on implementation on practical data.

Sequential Alternating Proximal Method for Scalable Sparse Structural SVMs

Structural Support Vector Machines (SSVMs) have recently gained wide prominence in classifying structured and complex objects like parse-trees, image segments and Part-of-Speech (POS) tags. Typical learning algorithms used in training SSVMs result in model parameters which are vectors residing in a large-dimensional feature space. Such a high-dimensional model parameter vector contains many non-zero components which often lead to slow prediction and storage issues. Hence there is a need for sparse parameter vectors which contain a very small number of non-zero components. L1-regularizer and elastic net regularizer have been traditionally used to get sparse model parameters. Though L1-regularized structural SVMs have been studied in the past, the use of elastic net regularizer for structural SVMs has not been explored yet. In this work, we formulate the elastic net SSVM and propose a sequential alternating proximal algorithm to solve the dual formulation. We compare the proposed method with existing methods for L1-regularized Structural SVMs. Experiments on large-scale benchmark datasets show that the proposed dual elastic net SSVM trained using the sequential alternating proximal algorithm scales well and results in highly sparse model parameters while achieving a comparable generalization performance. Hence the proposed sequential alternating proximal algorithm is a competitive method to achieve sparse model parameters and a comparable generalization performance when elastic net regularized Structural SVMs are used on very large datasets.

Multiagent based large data clustering scheme for data mining applications

Multiagent Systems consist of multiple computing elements called agents, which in order to achieve a given objective, can act on their own, react to the inputs, pro-act and cooperate. Data Mining deals with large data. Large data clustering is a data mining activity where in efficient clustering algorithms select a subset of original dataset as representative patterns. In the current work we propose a multi-agent based clustering scheme that combines multiple agents, each capable of generating a set of prototypes using an independent prototype selection algorithm. Each prototype set is used to predict the labels of unseen data. The results of these agents are combined by another agent resulting in a high classification accuracy. Such a scheme is of high practical utility in dealing with large datasets.

On simultaneous selection of prototypes and features in large data

In dealing with high-dimensional, large data, for the sake of abstract generation one resorts to either dimensionality reduction or cluster the patterns and deal with cluster representatives or both. The current paper examines whether there exists an equivalence in terms of generalization error. Four different approaches are followed and results of exercises are provided in driving home the issues involved.

Optimal skin detection for face localization using genetic algorithms

Face Recognition remains a challenging task over two decades in spite of many advances. Face Localization and Detection forms an important step in Face Recognition systems, face tracking in video surveillance, gesture analytics, etc. Many algorithms are in use for face detection each having their relative advantages and disadvantages. The challenges of the task emanate from with image specific variations in color components, light saturation, background clutter etc. In the current work, we focus on application of Genetic Algorithms for finding optimal limits for color components as well as de-saturation that leads to highly accurate skin color detection.

Scalable sequential alternating proximal methods for sparse structural SVMs and CRFs

Structural Support Vector Machines (SSVMs) and Conditional Random Fields (CRFs) are popular discriminative methods used for classifying structured and complex objects like parse trees, image segments and part-of-speech tags. The datasets involved are very large dimensional, and the models designed using typical training algorithms for SSVMs and CRFs are non-sparse. This non-sparse nature of models results in slow inference. Thus, there is a need to devise new algorithms for sparse SSVM and CRF classifier design. Use of elastic net and L1-regularizer has already been explored for solving primal CRF and SSVM problems, respectively, to design sparse classifiers. In this work, we focus on dual elastic net regularized SSVM and CRF. By exploiting the weakly coupled structure of these convex programming problems, we propose a new sequential alternating proximal (SAP) algorithm to solve these dual problems. This algorithm works by sequentially visiting each training set example and solving a simple subproblem restricted to a small subset of variables associated with that example. Numerical experiments on various benchmark sequence labeling datasets demonstrate that the proposed algorithm scales well. Further, the classifiers designed are sparser than those designed by solving the respective primal problems and demonstrate comparable generalization performance. Thus, the proposed SAP algorithm is a useful alternative for sparse SSVM and CRF classifier design.

Big Data Abstraction Through Multiagent Systems

Agent mining interaction has attracted a lot of attention among researchers. It is possible to solve large data mining problems through multiagent systems. Big data is characterized by huge volumes of data that are not easily amenable for generating abstraction; variety of data formats, data frequency, types of data, and their integration; real or near-real time data processing for generating business or scientific value depending on nature of data. Data mining algorithms and machine learning have a large role to play in big data abstraction. We propose to deal with big data with multiagent systems. In this process of suggesting possible ways of dealing big data problems using multiagent systems, we provide discussion on big data and algorithms associated with massive data systems such as MapReduce and PageRank. We discuss agents, multiagent systems, issues with big data analytics, and how the divide-and-conquer approach of multiagent systems improves handling huge datasets. We propose four multiagent systems that can help generating abstraction with big data. We provide suggested reading and bibliographic notes. A list of references is provided in the end.

Run-Length-Encoded Compression Scheme

Mining large datasets in a compressed domain are an interesting direction in data mining. In this chapter, we propose a nonlossy compression scheme. It is based on run-length encoding of binary-valued features or floating-point-valued features that are appropriately quantized into binary-valued data. The proposed algorithm compresses a given dataset in terms of runs and computes the dissimilarity in the compressed domain directly. This results in significant gains in computation time and storage. We provide a detailed discussion on relevant terms, algorithms, and its performance. We demonstrate efficiency of its working on classification of unseen compressed patterns. We discuss applicability of the scheme to genetic algorithms where classification happens to be a fitness function. We provide a few application scenarios in data mining. We provide theoretical discussions on the scheme. Bibliographic notes provide a brief discussion on important relevant references. A list of references is provided in the end.