Latent semantic indexing is an optimal special case of multidimensional scaling
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Latent semantic indexing: a probabilistic analysis
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A similarity-based probability model for latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Latent semantic space: iterative scaling improves precision of inter-document similarity measurement
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Locality preserving indexing for document representation
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Polynomial filtering in latent semantic indexing for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
On scaling latent semantic indexing for large peer-to-peer systems
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Principles of hash-based text retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regularized locality preserving indexing via spectral regression
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Semi-supervised dimensionality reduction in image feature space
Proceedings of the 2008 ACM symposium on Applied computing
Iterative Search for Similar Documents on Mobile Devices
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Locality condensation: a new dimensionality reduction method for image retrieval
MM '08 Proceedings of the 16th ACM international conference on Multimedia
A New Solution Scheme of Unsupervised Locality Preserving Projection Method for the SSS Problem
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Information Sciences: an International Journal
Cascaded search for similar documents between mobile devices
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Semi-supervised orthogonal discriminant analysis via label propagation
Pattern Recognition
To obtain orthogonal feature extraction using training data selection
Proceedings of the 18th ACM conference on Information and knowledge management
Entropy controlled Laplacian regularization for least square regression
Signal Processing
A rank-one update algorithm for fast solving kernel Foley-Sammon optimal discriminant vectors
IEEE Transactions on Neural Networks
LPP solution schemes for use with face recognition
Pattern Recognition
TAKES: a fast method to select features in the kernel space
Proceedings of the 20th ACM international conference on Information and knowledge management
Local maximal margin discriminant embedding for face recognition
Journal of Visual Communication and Image Representation
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We consider the problem of document indexing and representation. Recently, Locality Preserving Indexing (LPI) was proposed for learning a compact document subspace. Different from Latent Semantic Indexing which is optimal in the sense of global Euclidean structure, LPI is optimal in the sense of local manifold structure. However, LPI is extremely sensitive to the number of dimensions. This makes it difficult to estimate the intrinsic dimensionality, while inaccurately estimated dimensionality would drastically degrade its performance. One reason leading to this problem is that LPI is non-orthogonal. Non-orthogonality distorts the metric structure of the document space. In this paper, we propose a new algorithm called Orthogonal LPI. Orthogonal LPI iteratively computes the mutually orthogonal basis functions which respect the local geometrical structure. Moreover, our empirical study shows that OLPI can have more locality preserving power than LPI. We compare the new algorithm to LSI and LPI. Extensive experimental results show that Orthogonal LPI obtains better performance than both LSI and LPI. More crucially, it is insensitive to the number of dimensions, which makes it an efficient data preprocessing method for text clustering, classification, retrieval, etc.