Y. Linde

Bio: Y. Linde is an academic researcher from Codex Corporation. The author has contributed to research in topics: Speech coding & Vector quantization. The author has an hindex of 6, co-authored 6 publications receiving 8321 citations.

An Algorithm for Vector Quantizer Design

Abstract: An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data. The basic properties of the algorithm are discussed and demonstrated by examples. Quite general distortion measures and long blocklengths are allowed, as exemplified by the design of parameter vector quantizers of ten-dimensional vectors arising in Linear Predictive Coded (LPC) speech compression with a complicated distortion measure arising in LPC analysis that does not depend only on the error vector.

Locally optimal block quantizer design

Abstract: Several properties are developed for a recently proposed algorithm for the design of block quantizers based either on a probabilistic source model or on a long training sequence of data. Conditions on the source and general distortion measures under which the algorithm is well defined and converges to a local minimum are provided. A variation of the ergodic theorem is used to show that if the source is block stationary and ergodic, then in the limit as n → ∝, the algorithm run on a sample distribution of a training sequence of length n will produce the same result as if the algorithm were run on the “true” underlying distribution.

Vector Quantizers and Predictive Quantizers for Gauss-Markov Sources

Abstract: Low-rate vector quantizers are designed and simulated for highly correlated Gauss-Markov sources and the resulting performance is compared with Arnstein's optimized predictive quantizer and with Huang and Schultheiss' optimized transform coder. Two implementations of vector quantizers are considered: full search vector quantizers-which are optimal but require large codebook searches-and tree searched vector quantizers-which are suboptimal but require far less searching. The various systems are compared on the basis of performance, complexity, and generality of design techniques.

The Design of Trellis Waveform Coders

Abstract: New algorithms for the design of trellis encoding data compression systems are described. The mare algorithm uses a training sequence of actual data from a source to improve an initial trellis decoder. An additional algorithm extends the constraint length of a given decoder. Combined, these algorithms allow the automatic design of a trellis encoding system for a particular source. The algorithms' effectiveness for random sources is demonstrated through performance comparisons with other source coding systems and with theoretical bounds. The algorithms are applied to the practical problem of the design of trellis and hybrid codes for medium-to-lowrate speech compression.

A Fake Process Approach to Data Compression

Abstract: The problem of designing a good decoder for a timeinvariant tree-coding data compression system is equivalent to that of finding a good low rate "fake process" for the original source, where the fake is produced by a time-invariant nonlinear filtering of an independent, identically distributed sequence of uniformly distributed discrete random variables and "goodness" is measured by the \bar{\rho} -distance between the fake and the original source. Several simple ad hoc techniques for obtaining such fake processes are introduced and shown by simulation to provide an improvement of typically 1-2 dB over optimum quantization, delta modulation, and predictive quantization for one-bit per symbol compression of Gaussian memoryless, autoregressive, and moving average sources. In addition, the fake process viewpoint provides a new intuitive explanation of why delta modulation and predictive quantization work as well as they do on Gaussian autoregressive sources.

The self-organizing map

Abstract: The self-organized map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications. The self-organizing map has the property of effectively creating spatially organized internal representations of various features of input signals and their abstractions. One result of this is that the self-organization process can discover semantic relationships in sentences. Brain maps, semantic maps, and early work on competitive learning are reviewed. The self-organizing map algorithm (an algorithm which order responses spatially) is reviewed, focusing on best matching cell selection and adaptation of the weight vectors. Suggestions for applying the self-organizing map algorithm, demonstrations of the ordering process, and an example of hierarchical clustering of data are presented. Fine tuning the map by learning vector quantization is addressed. The use of self-organized maps in practical speech recognition and a simulation experiment on semantic mapping are discussed. >

Data clustering: 50 years beyond K-means

Abstract: Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into a system of ranked taxa: domain, kingdom, phylum, class, etc. Cluster analysis is the formal study of methods and algorithms for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning). The aim of clustering is to find structure in data and is therefore exploratory in nature. Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, K-means, was first published in 1955. In spite of the fact that K-means was proposed over 50 years ago and thousands of clustering algorithms have been published since then, K-means is still widely used. This speaks to the difficulty in designing a general purpose clustering algorithm and the ill-posed problem of clustering. We provide a brief overview of clustering, summarize well known clustering methods, discuss the major challenges and key issues in designing clustering algorithms, and point out some of the emerging and useful research directions, including semi-supervised clustering, ensemble clustering, simultaneous feature selection during data clustering, and large scale data clustering.

Data Clustering: 50 Years Beyond K-means

Abstract: The practice of classifying objects according to perceived similarities is the basis for much of science. Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms in to taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and methods for grouping objects according to measured or perceived intrinsic characteristics. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes cluster analysis (unsupervised learning) from discriminant analysis (supervised learning). The objective of cluster analysis is to simply find a convenient and valid organization of the data, not to establish rules for separating future data into categories.