Irfan Essa’s Academic Activities

Pei Yin, Irfan Essa, James Rehg, Thad Starner (2008) “Discriminative Feature Selection for Hidden Markov Models using Segmental Boosting”, ICASSP 2008 - March 30 - April 4, 2008 - Las Vegas, Nevada, U.S.A. (Paper: MLSP-P3.D8, Session: Pattern Recognition and Classification II, Time: Thursday, April 3, 15:30 - 17:30, Topic: Machine Learning for Signal Processing: Learning Theory and Modeling) (PDF|Project Site)

ABSTRACT

We address the feature selection problem for hidden Markov models (HMMs) in sequence classification. Temporal correlation in sequences often causes difficulty in applying feature selection techniques. Inspired by segmental k-means segmentation (SKS), we propose Segmentally Boosted HMMs (SBHMMs), where the state-optimized features are constructed in a segmental and discriminative manner. The contributions are twofold. First, we introduce a novel feature selection algorithm, where the temporal dynamics are decoupled from the static learning procedure by assuming that the sequential data are piecewise independent and identically distributed. Second, we show that the SBHMM consistently improves traditional HMM recognition in various domains. The reduction of error compared to traditional HMMs ranges from 17% to 70% in American Sign Language recognition, human gait identification, lip reading, and speech recognition.

D. Minnen, I. Essa, C.L. Isbell, and T. Starner “Detecting Subdimensional Motifs: An Efficient Algorithm for Generalized Multivariate Pattern Discovery” In IEEE Int. Conf. on Data Mining (ICDM) 2007, Omaha, NE, October 28-31, 2007. [PDF]

Abstract

Discovering recurring patterns in time series data is a fundamental problem for temporal data mining. This paper addresses the problem of locating subdimensional motifs in real-valued, multivariate time series, which requires the simultaneous discovery of sets of recurring patterns along with the corresponding relevant dimensions. While many approaches to motif discovery have been developed, most are restricted to categorical data, univariate time series, or multivariate data in which the temporal patterns span all of the dimensions. In this paper, we present an expected linear-time algorithm that addresses a generalization of multivariate pattern discovery in which each motif may span only a subset of the dimensions. To validate our algorithm, we discuss its theoretical properties and empirically evaluate it using several data sets including synthetic data and motion capture data collected by an on-body inertial sensor.

Discovering Multivariate Motifs using Subsequence Density Estimation and Greedy Mixture Learning

Abstract

The problem of locating motifs in real-valued, multivariate time series data involves the discovery of sets of recurring patterns embedded in the time series. Each set is composed of several non-overlapping subsequences and constitutes a motif because all of the included subsequences are similar. The ability to automatically discover such motifs allows intelligent systems to form endogenously meaningful representations of their environment through unsupervised sensor analysis. In this paper, we formulate a unifying view of motif discovery as a problem of locating regions of high density in the space of all time series subsequences. Our approach is efficient (sub-quadratic in the length of the data), requires fewer user-specified parameters than previous methods, and naturally allows variable length motif occurrences and nonlinear temporal warping. We evaluate the performance of our approach using four data sets from different domains including on-body inertial sensors and speech.

• D. Minnen, C.L. Isbell, I. Essa, and T. Starner Discovering Multivariate Motifs using Subsequence Density Estimation and Greedy Mixture Learning Twenty-Second Conf. on Artificial Intelligence (AAAI-07), Vancouver, B.C., July 22-26, 2007.

Discovering Characteristic Actions from On-Body Sensor Data (IEEEXplore)

Minnen, D. Starner, T. Essa, I. Isbell, C.
College of Computing, Georgia Institute of Technology, Atlanta, GA 30332 USA. dminn@cc.gatech.edu
This paper appears in: Wearable Computers, 2006 10th IEEE International Symposium on
Publication Date: Oct. 2006
On page(s): 11 - 18
Number of Pages: 11 - 18
Location: Montreux, Switzerland
ISSN: 1550-4816
ISBN: 1-4244-0598-x
Digital Object Identifier: 10.1109/ISWC.2006.286337
Posted online: 2007-01-22 09:58:15.0

Abstract

We present an approach to activity discovery, the unsupervised identification and modeling of human actions embedded in a larger sensor stream. Activity discovery can be seen as the inverse of the activity recognition problem. Rather than learn models from hand-labeled sequences, we attempt to discover motifs, sets of similar subsequences within the raw sensor stream, without the benefit of labels or manual segmentation. These motifs are statistically unlikely and thus typically correspond to important or characteristic actions within the activity. The problem of activity discovery differs from typicalmotif discovery, such as locating protein binding sites, because of the nature of time series data representing human activity. For example, in activity data, motifs will tend to be sparsely distributed, vary in length, and may only exhibit intra-motif similarity after appropriate time warping. In this paper, we motivate the activity discovery problem and present our approach for efficient discovery of meaningful actions from sensor data representing human activity. We empirically evaluate the approach on an exercise data set captured by a wrist-mounted, three-axis inertial sensor. Our algorithm successfully discovers motifs that correspond to the real exercises with a recall rate of 96.3% and overall accuracy of 86.7% over six exercises and 864 occurrences.