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- Jieping Ye, Ravi Janardan, Qi Li
- NIPS
- 2004

Linear Discriminant Analysis (LDA) is a well-known scheme for feature extraction and dimension reduction. It has been used widely in many applications involving high-dimensional data, such as face recognition and image retrieval. An intrinsic limitation of classical LDA is the so-called singularity problem, that is, it fails when all scatter matrices are… (More)

- Jieping Ye, Ravi Janardan, Qi Li
- KDD
- 2004

Recent years have witnessed a dramatic increase in the quantity of image data collected, due to advances in fields such as medical imaging, reconnaissance, surveillance, astronomy, multimedia etc. With this increase has come the need to be able to store, transmit, and query large volumes of image data efficiently. A common operation on image databases is… (More)

- Prosenjit Gupta, Ravi Janardan, Michiel H. M. Smid
- J. Algorithms
- 1993

In a generalized intersection searching problem, a set, S, of colored geometrie objects is to be preprocessed so that given some query object, q, the distinct colors of the objects intersected by q can be reported efficiently or the number of such colors can be counted effi.ciently. In the dynamic setting, colored objects can be inserted into or de1eted… (More)

- Jieping Ye, Ravi Janardan, Cheong Hee Park, Haesun Park
- IEEE Transactions on Pattern Analysis and Machine…
- 2004

An optimization criterion is presented for discriminant analysis. The criterion extends the optimization criteria of the classical Linear Discriminant Analysis (LDA) through the use of the pseudoinverse when the scatter matrices are singular. It is applicable regardless of the relative sizes of the data dimension and sample size, overcoming a limitation of… (More)

- Siu-Wing Cheng, Ravi Janardan
- SIAM J. Comput.
- 1990

A point location scheme is presented for an n-veriet dynamic planar subdivision whose underlying graph is only required to be connected. The scheme uses O(n) space, yields an O(log2 n) query time and an O(1ogn) update time. Insertion (resp. deleiion) of an arbitrary k-edge chain inside a region can be performed in O(klog(n+k)) (resp. O(k1ogn)) iime. The… (More)

- Ravi Janardan, Mario A. López
- Int. J. Comput. Geometry Appl.
- 1993

- Jieping Ye, Ravi Janardan, Qi Li, Haesun Park
- IEEE Transactions on Knowledge and Data…
- 2006

High-dimensional data appear in many applications of data mining, machine learning, and bioinformatics. Feature reduction is commonly applied as a preprocessing step to overcome the curse of dimensionality. Uncorrelated linear discriminant analysis (ULDA) was recently proposed for feature reduction. The extracted features via ULDA were shown to be… (More)

- Jieping Ye, Qi Li, Hui Xiong, Haesun Park, Ravi Janardan, Vipin Kumar
- IEEE Transactions on Knowledge and Data…
- 2004

Dimension reduction is critical for many database and data mining applications, such as efficient storage and retrieval of high-dimensional data. In the literature, a well-known dimension reduction scheme is Linear Discriminant Analysis (LDA). The common aspect of previously proposed LDA based algorithms is the use of Singular Value Decomposition (SVD). Due… (More)

- Jieping Ye, Tao Li, Tao Xiong, Ravi Janardan
- IEEE/ACM Transactions on Computational Biology…
- 2004

The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high (in the thousands) compared to the number of data samples (in the tens or low hundreds); that is, the data dimension is large compared to the number of… (More)

- Greg N. Frederickson, Ravi Janardan
- Algorithmica
- 1988

Classes of network topologies are identified in which shortest-path information can be succinctly stored at the nodes, if they are assigned suitable names. The naming allows each edge at a node to be labeled with zero or more intervals of integers, representing all nodes reachable by a shortest path via that edge. Starting with the class of outerplanar… (More)